CN111610417A - Discharge signal source separation method based on community discovery - Google Patents

Discharge signal source separation method based on community discovery Download PDF

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CN111610417A
CN111610417A CN202010468639.8A CN202010468639A CN111610417A CN 111610417 A CN111610417 A CN 111610417A CN 202010468639 A CN202010468639 A CN 202010468639A CN 111610417 A CN111610417 A CN 111610417A
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community
sensor
discharge
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CN111610417B (en
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黄成军
郭灿新
黄志方
邵震宇
刘丹丹
豆慧玲
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Huacheng Electrical Technology Co ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
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    • G01—MEASURING; TESTING
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Abstract

The invention discloses a discharge signal source separation method based on community discovery, which comprises the following steps: the method comprises the steps of constructing a topological graph of a sensor, acquiring nodes of a community network, constructing the community network and separating a discharge signal source. According to the discharge signal source separation method based on community discovery, the characteristic information of the monitoring data is analyzed, the position characteristic information of the sensor is introduced, the characteristic source is not single any more, and the separation accuracy can be effectively improved; the similarity algorithm is applied, and the concept of community discovery is introduced to be applied to the cluster analysis of the signal sources, so that the number and the distribution positions of the signal sources can be accurately identified, the diagnosis and analysis efficiency is improved, and an accurate data basis is provided for the positioning of defects; the method can be applied to GIS partial discharge signal positioning based on the ultrahigh frequency sensor, does not need the sensor to synchronously acquire signals, and has wide application range.

Description

Discharge signal source separation method based on community discovery
Technical Field
The invention relates to a discharge signal source separation method based on community discovery.
Background
With the rapid development of economy in China, the demand for electricity in society is increasing, the scale of power system equipment is expanding, and the contradictory urban construction land is increasingly tense. GIS (Gas Insulation Switchgear) is more and more widely applied at home and abroad in recent decades due to the advantages of small occupied area, small influence from external environment, safe and reliable operation, simple maintenance and the like. However, in the manufacturing and assembling process of the GIS, some small defects such as metal particles, insulation air gaps, etc. are left inside the GIS due to problems of process, design, etc., and these small defects may be developed into dangerous discharge channels during the operation of the GIS, and finally cause insulation breakdown accidents. Meanwhile, the GIS is a metal closed device, so that the health condition of the GIS is difficult to evaluate, and once a fault occurs, a disassembly mode is needed to determine the defect condition, so that the GIS is difficult to overhaul and consumes long time. Therefore, the method has very important significance for monitoring partial discharge and positioning partial discharge defects of the GIS in operation in order to prevent insulation faults of GIS equipment and guarantee safe operation of a power system.
In the GIS partial discharge diagnosis and positioning process, because various interferences exist in the GIS operating environment, a plurality of partial discharge sources inside the GIS can be provided, and the simultaneous existence of various partial discharge sources and interference signals brings great difficulty to the positioning of the partial discharge sources, the judgment of data homology and the separation of a plurality of partial discharge sources are key steps influencing the positioning accuracy. Most of the existing GIS local discharge source positioning methods aim at an online monitoring device and require that all sensors synchronously acquire data, and the discharge source separation method applied to the positioning method is only suitable for the condition of data synchronization; the current discharge source separation method achieves the purpose of separating the discharge source only by analyzing the characteristics of the collected data, the source of the characteristic data is single, and the separation accuracy is poor. Patent CN201811498702.1 discloses a detection method, system and data fusion analysis unit for partial discharge signal source, wherein the mentioned signal source grouping method requires synchronous signal acquisition by sensors, and is limited in application in the field where synchronous signal acquisition is impossible; the signal grouping in the method completely depends on the data characteristics, which can cause the situation that different signal sources are classified into one type due to similar signal characteristics, thereby reducing the accuracy of signal source grouping; the clustering algorithm adopted in the method carries out clustering grouping on all the collected signals, does not distinguish abnormal signals from normal signals, and has poor clustering effect. Patent CN201510056192.2 discloses a GIS partial discharge positioning method based on ultrahigh frequency discharge online monitoring data, which realizes the separation of discharge sources by performing correlation analysis on the daily average discharge repetition rate and the daily average discharge amount of data collected by a sensor, and the characteristic data only adopts the time sequence of the data, and has single characteristic source and poor separation accuracy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a discharge signal source separation method based on community discovery, which introduces position characteristic information of a sensor besides characteristic information of analysis monitoring data, so that the characteristic source is not single any more, and the separation accuracy can be effectively improved; the similarity algorithm is applied, and the concept of community discovery is introduced to be applied to the cluster analysis of the signal sources, so that the number and the distribution positions of the signal sources can be accurately identified, the diagnosis and analysis efficiency is improved, and an accurate data basis is provided for the positioning of defects; the method can be applied to GIS partial discharge signal positioning based on the ultrahigh frequency sensor, does not need the sensor to synchronously acquire signals, and has wide application range.
The technical scheme for realizing the purpose is as follows: a discharge signal source separation method based on community discovery comprises the following steps:
s1, constructing a topological graph of the sensor: respectively coding a plurality of sensors on a GIS body and the surrounding walls thereof, associating the coded sensors with GIS spatial positions according to a GIS spatial structure, an electrical connection diagram and sensor installation positions, and establishing a sensor spatial topological diagram, wherein the sensors are nodes of the topological diagram, two directly and electrically connected sensors are connected by straight lines to form side lines of the topological diagram, and the length of each side line represents the distance of electrical connection between the two corresponding sensors;
s2, acquiring nodes of the community network: and finding out the associated sensor of the abnormal sensor on the signal propagation path by combining the sensor space topological graph established in the step S1, which comprises the following specific steps:
s21, selecting 2 abnormal sensors;
s22, calculating the shortest path among the 2 abnormal sensors by utilizing a Dijkstra algorithm;
s23, calculating the number k of sensors on the shortest path, and when k is less than or equal to 2, judging that the sensors on the shortest path among the 2 abnormal sensors are related sensors; when k is greater than 2, judging that no correlation sensor exists among the 2 abnormal sensors;
s24, repeating the steps S21-S23 to obtain the associated sensors among all the abnormal sensors, wherein the abnormal sensors and the associated sensors are the nodes of the community network;
s3, constructing a community network step: the method for clustering and analyzing the partial discharge signals by using the combination of the feature similarity and community discovery is used for judging the homology of data, separating a plurality of partial discharge sources and constructing a community network, and specifically comprises the following steps:
s31, homology analysis of nodes of the community network: according to the attenuation characteristics of the electromagnetic waves, the intensity of the electromagnetic wave signals emitted by the same discharge source can be attenuated to different degrees along the propagation path, but the variation trends of the electromagnetic wave signals are the same, and homology analysis is performed by comparing the correlation of characteristic sequences of monitoring data of different sensors;
s32, constructing a single community network: adding a sideline between two nodes judged as the same discharge source to form 1 community network;
s33, repeating the step S32 to obtain a single community network among other nodes, and continuously combining and overlapping all the single community networks to obtain a final community network;
s4, separating a discharge signal source step: the method comprises the steps that a signal community structure is determined according to a final community network, separation of discharge sources is completed, 1 signal community is formed by nodes connected with edges in the community network, 1 signal community is formed by nodes connected without edges independently, and each signal community corresponds to an abnormal signal source.
2. The method for separating discharge signal sources based on community discovery as claimed in claim 1, wherein in step 31, the characteristic sequence includes a daily average discharge amplitude sequence and a daily average discharge number sequence.
3. The method for separating discharging signal sources based on community discovery as claimed in claim 2, wherein the step S31 specifically includes the steps of:
s311, acquiring local discharge monitoring data of the community network node in historical m days;
s312, calculating a daily average discharge amplitude sequence and a daily average discharge frequency sequence of historical m days;
s313, carrying out normalization processing on the daily average discharge amplitude sequence and the daily average discharge frequency sequence, wherein the normalization formula is as follows:
Figure BDA0002513497290000031
in the formula, x*Is the value after the normalization of each vector in the feature sequence, x is the value of each vector in the feature sequence, xminIs the minimum value of the vector in the feature sequence, xmaxThe maximum value of the vector in the characteristic sequence;
normalizing the quantities in the characteristic sequence to obtain a normalized daily average discharge amplitude sequence and a normalized daily average discharge frequency sequence;
s314, calculating the daily average discharge amplitude similarity r between every two community network nodescSimilarity r with daily average discharge frequencydThe calculation formula is as follows:
Figure BDA0002513497290000041
wherein r is the similarity of A, B sensor characteristic sequences, A (t) is the characteristic sequence of A sensor,
Figure BDA0002513497290000042
is a mean sequence of A (t), B(t) is the characteristic sequence of the B sensor,
Figure BDA0002513497290000043
is the mean sequence of B (t);
s315, judging the homology of the signals, and judging that the abnormal data monitored by the two sensors have correlation when the similarity satisfies the following formula and come from the same discharge source:
rc∩rd>
in the formula, the similarity threshold is more than or equal to 0.6 and less than or equal to 0.9.
4. The method for separating discharge signal sources based on community discovery as claimed in claim 1, wherein in step S1, the sensor spatial topology map includes not only spatial location information of sensors and GIS, but also electrical connection information of each sensor on a propagation path of partial discharge signals.
According to the electromagnetic wave attenuation characteristics, although the intensity of the electromagnetic wave signals emitted by the same discharge source is attenuated to different degrees along the propagation path, the electromagnetic wave signals emitted by the same discharge source have similar characteristics. By utilizing the characteristic that the characteristics of electromagnetic waves emitted by the same discharge source are similar, the invention designs a discharge signal source separation method based on community discovery, which is applied to GIS partial discharge signal positioning based on an ultrahigh frequency sensor to separate abnormal discharge sources.
According to the discharge signal source separation method based on community discovery, the characteristic information of the monitoring data is analyzed, the position characteristic information of the sensor is introduced, the characteristic source is not single any more, and the separation accuracy can be effectively improved; the similarity algorithm is applied, and the concept of community discovery is introduced to be applied to the cluster analysis of the signal sources, so that the number and the distribution positions of the signal sources can be accurately identified, the diagnosis and analysis efficiency is improved, and an accurate data basis is provided for the positioning of defects; the method can be applied to GIS partial discharge signal positioning based on the ultrahigh frequency sensor, does not need the sensor to synchronously acquire signals, and has wide application range.
Drawings
FIG. 1 is a flow chart of a discharge signal source separation method based on community discovery according to the present invention;
FIG. 2 is a diagram of spatial location information of a sensor on a GIS;
FIG. 3 is a sensor spatial topology;
FIG. 4 is a schematic diagram of a single community network formed by every two nodes;
FIG. 5 is a schematic diagram of an abnormal sensor community network.
Detailed Description
In order that those skilled in the art will better understand the technical solution of the present invention, the following detailed description is given with reference to the accompanying drawings:
the process of finding out the community structure of a given community network is called community discovery, the community structure in the network can be divided through the community discovery, and the 'affinity and sparseness' of the community interval is judged, so the community discovery can also be regarded as a 'clustering algorithm'. According to the discharge signal source separation method based on community discovery, the concept of community discovery is introduced to separate signal sources, a community network of abnormal sensors is established by combining the spatial position characteristics of the sensors and the acquired abnormal data characteristics, the homology of community network nodes is analyzed, the structure of a signal community is determined, the number of abnormal discharge sources and a sensor group associated with a local discharge source are obtained, and a basis is provided for accurate positioning of the local discharge sources.
Referring to fig. 1, fig. 2, fig. 3, fig. 4 and fig. 5, in an embodiment of the present invention, a discharge signal source separation method based on community discovery includes the following steps:
s1, constructing a topological graph of the sensor: installing 8 partial discharge ultrahigh frequency sensors on a GIS body, wherein the number is 1-8, installing 2 spatial ultrahigh frequency sensors on the wall around the GIS, the number is 9-10 (see figure 2), associating the coded sensors with the GIS spatial position according to the GIS spatial structure, an electrical connection diagram and the sensor installation position, and establishing a sensor spatial topological diagram (see figure 3), wherein the sensors are nodes of the topological diagram, two sensors which are directly and electrically connected form a side line of the topological diagram by linear connection, and the length of the side line represents the distance of electrical connection between the two corresponding sensors; the sensor space topological graph not only comprises the space position information of the sensors and the GIS, but also comprises the electrical connection information of each sensor on a partial discharge signal propagation path;
s2, acquiring nodes of the community network: in this example, numbers of sensors with abnormal data (referred to as abnormal sensors for short) are set to be 1, 3, 4, and 8, and a sensor space topological graph established in step S1 is combined to find a related sensor of the abnormal sensor on a signal propagation path, which includes the following steps:
s21, selecting 2 abnormal sensors;
s22, calculating the shortest path between 2 abnormal sensors using Dijkstra algorithm, as shown in table 1:
sensor with a sensor element 1 3 4 8
1 - 1-3 1-4 1-10-8
3 - - 3-4 3-10-8
4 - - - 4-5-7-8
TABLE 1
S23, calculating the number k of sensors on the shortest path, and when k is less than or equal to 2, judging that the sensors on the shortest path among the 2 abnormal sensors are related sensors; when k is greater than 2, judging that no correlation sensor exists among the 2 abnormal sensors; the correlated sensors are 3 and 10 from table 1;
s24, repeating the steps S21-S23 to obtain the associated sensors among all the abnormal sensors, wherein the abnormal sensors and the associated sensors are the nodes of the community network; the anomaly sensors 1, 3, 4, 8 and the associated sensors 10 are nodes of a community network.
S3, constructing a community network step: various interferences exist in the operating environment of the GIS, and the number of partial discharge sources inside the GIS may be 1 or more, and the simultaneous existence of various types of partial discharge sources and interference signals brings great difficulty to the positioning of the partial discharge sources. The method for clustering and analyzing the partial discharge signals by using the combination of the feature similarity and community discovery comprises the following steps of:
s31, homology analysis of nodes of the community network: according to the attenuation characteristics of the electromagnetic waves, the intensity of the electromagnetic wave signals emitted by the same discharge source can be attenuated to different degrees along the propagation path, but the variation trends of the electromagnetic wave signals are the same, and homology analysis is performed by comparing the correlation of characteristic sequences of monitoring data of different sensors; the method specifically comprises the following steps:
s311, acquiring local discharge monitoring data of the community network node for historical m days, wherein m is 30 in the example;
s312, calculating a daily average discharge amplitude sequence and a daily average discharge frequency sequence of historical m days;
s313, carrying out normalization processing on the daily average discharge amplitude sequence and the daily average discharge frequency sequence, wherein the normalization formula is as follows:
Figure BDA0002513497290000061
in the formula (1), x*Is the value after the normalization of each vector in the feature sequence, x is the value of each vector in the feature sequence, xminIs the minimum value of the vector in the feature sequence, xmaxThe maximum value of the vector in the characteristic sequence;
normalizing the quantities in the characteristic sequence to obtain a normalized daily average discharge amplitude sequence and a normalized daily average discharge frequency sequence;
s314, calculating the daily average discharge amplitude similarity r between every two community network nodescSimilarity r with daily average discharge frequencydThe calculation formula is as follows:
Figure BDA0002513497290000071
wherein r is the similarity of A, B sensor characteristic sequences, A (t) is the characteristic sequence of A sensor,
Figure BDA0002513497290000072
is the mean value sequence of A (t), B (t) is the characteristic sequence of the B sensor,
Figure BDA0002513497290000073
is the mean sequence of B (t);
r between sensors at two ends of each edge in the abnormal sensor topological graph is calculated and obtained through a formula (1) and a formula (2)cAnd rdR between the sensorscAs shown in table 2:
sensor with a sensor element 1 3 4 8 10
1 - 0.8438 0.8785 0.3215 0.3441
3 - - 0.9034 0.3167 0.3479
4 - - - 0.3074 0.3085
8 - - - - 0.6932
TABLE 2
R between sensorsdAs shown in table 3:
sensor with a sensor element 1 3 4 8 10
1 - 0.8945 0.9013 0.3135 0.5879
3 - - 0.9241 0.2986 0.2812
4 - - - 0.2945 0.2887
8 - - - - 0.7259
TABLE 3
S315, judging the homology of the signals, and judging that the abnormal data monitored by the two sensors have correlation when the similarity satisfies the following formula and come from the same discharge source:
rc∩rd>(3)
in the formula, the similarity threshold is not less than 0.6 and not more than 0.9, in this example, 0.65 is taken.
Comparing the similarity of tables 2 and 3 using equation (3) yields that sensors 1, 3, 4 have homology and sensors 8 and 10 have homology.
S32, constructing a single community network: adding a sideline between two nodes judged as the same discharge source to form 1 community network;
s33, repeating the step S32 to obtain a single community network among other nodes (see FIG. 4), and continuously merging and overlapping all the single community networks to obtain a final community network (see FIG. 5);
s4, separating a discharge signal source step: the method comprises the steps that a signal community structure is determined according to a final community network, separation of discharge sources is completed, 1 signal community is formed by nodes connected with edges in the community network, 1 signal community is formed by nodes connected without edges independently, and each signal community corresponds to an abnormal signal source.
Thus, 2 signal communities, namely the signal community composed of the sensors 1, 3 and 4 and the signal community composed of the sensors 8 and 10, can be separated out in the present example. The 2 signal communities represent 2 abnormal discharge sources, and the discharge sources are located in the areas near the sensors corresponding to the signal communities.
In conclusion, the discharge signal source separation method based on community discovery introduces the position characteristic information of the sensor besides the characteristic information of the analysis monitoring data, the characteristic source is not single any more, and the separation accuracy can be effectively improved; the similarity algorithm is applied, and the concept of community discovery is introduced to be applied to the cluster analysis of the signal sources, so that the number and the distribution positions of the signal sources can be accurately identified, the diagnosis and analysis efficiency is improved, and an accurate data basis is provided for the positioning of defects; the method can be applied to GIS partial discharge signal positioning based on the ultrahigh frequency sensor, does not need the sensor to synchronously acquire signals, and has wide application range.
It should be understood by those skilled in the art that the above embodiments are only for illustrating the present invention and are not to be used as a limitation of the present invention, and that changes and modifications to the above described embodiments are within the scope of the claims of the present invention as long as they are within the spirit and scope of the present invention.

Claims (4)

1. A discharge signal source separation method based on community discovery is characterized by comprising the following steps:
s1, constructing a topological graph of the sensor: installing a plurality of sensors on a GIS body and the surrounding walls of the GIS body respectively, coding all the sensors, associating the coded sensors with GIS spatial positions according to a GIS spatial structure, an electrical connection diagram and sensor installation positions, and establishing a sensor spatial topological diagram, wherein the sensors are nodes of the topological diagram, two directly and electrically connected sensors are connected by straight lines to form side lines of the topological diagram, and the length of each side line represents the distance of electrical connection between the two corresponding sensors;
s2, acquiring nodes of the community network: and finding out the associated sensor of the abnormal sensor on the signal propagation path by combining the sensor space topological graph established in the step S1, which comprises the following specific steps:
s21, selecting 2 abnormal sensors;
s22, calculating the shortest path among the 2 abnormal sensors by utilizing a Dijkstra algorithm;
s23, calculating the number k of sensors on the shortest path, and when k is less than or equal to 2, judging that the sensors on the shortest path among the 2 abnormal sensors are related sensors; when k is greater than 2, judging that no correlation sensor exists among the 2 abnormal sensors;
s24, repeating the steps S21-S23 to obtain the associated sensors among all the abnormal sensors, wherein the abnormal sensors and the associated sensors are the nodes of the community network;
s3, constructing a community network step: the method for clustering and analyzing the partial discharge signals by using the combination of the feature similarity and community discovery is used for judging the homology of data, separating a plurality of partial discharge sources and constructing a community network, and specifically comprises the following steps:
s31, homology analysis of nodes of the community network: according to the attenuation characteristics of the electromagnetic waves, the intensity of the electromagnetic wave signals emitted by the same discharge source can be attenuated to different degrees along the propagation path, but the variation trends of the electromagnetic wave signals are the same, and homology analysis is performed by comparing the correlation of characteristic sequences of monitoring data of different sensors;
s32, constructing a single community network: adding a sideline between two nodes judged as the same discharge source to form 1 community network;
s33, repeating the step S32 to obtain a single community network among other nodes, and continuously combining and overlapping all the single community networks to obtain a final community network;
s4, separating a discharge signal source step: the method comprises the steps that a signal community structure is determined according to a final community network, separation of discharge sources is completed, 1 signal community is formed by nodes connected with edges in the community network, 1 signal community is formed by nodes connected without edges independently, and each signal community corresponds to an abnormal signal source.
2. The method for separating discharge signal sources based on community discovery as claimed in claim 1, wherein in step 31, the characteristic sequence includes a daily average discharge amplitude sequence and a daily average discharge number sequence.
3. The method for separating discharging signal sources based on community discovery as claimed in claim 2, wherein the step S31 specifically includes the steps of:
s311, acquiring local discharge monitoring data of the community network node in historical m days;
s312, calculating a daily average discharge amplitude sequence and a daily average discharge frequency sequence of historical m days;
s313, carrying out normalization processing on the daily average discharge amplitude sequence and the daily average discharge frequency sequence, wherein the normalization formula is as follows:
Figure FDA0002513497280000021
in the formula, x*Is the value after the normalization of each vector in the feature sequence, x is the value of each vector in the feature sequence, xminIs the minimum value of the vector in the feature sequence, xmaxThe maximum value of the vector in the characteristic sequence;
normalizing the quantities in the characteristic sequence to obtain a normalized daily average discharge amplitude sequence and a normalized daily average discharge frequency sequence;
s314, calculating the daily average discharge amplitude similarity r between every two community network nodescSimilarity r with daily average discharge frequencydThe calculation formula is as follows:
Figure FDA0002513497280000022
wherein r is the similarity of A, B sensor characteristic sequences, A (t) is the characteristic sequence of A sensor,
Figure FDA0002513497280000023
is the mean value sequence of A (t), B (t) is the characteristic sequence of the B sensor,
Figure FDA0002513497280000024
is the mean sequence of B (t);
s315, judging the homology of the signals, and judging that the abnormal data monitored by the two sensors have correlation when the similarity satisfies the following formula and come from the same discharge source:
rc∩rd>
in the formula, the similarity threshold is more than or equal to 0.6 and less than or equal to 0.9.
4. The method for separating discharge signal sources based on community discovery as claimed in claim 1, wherein in step S1, the sensor spatial topology map includes not only spatial location information of sensors and GIS, but also electrical connection information of each sensor on a propagation path of partial discharge signals.
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