CN110929751B - Current transformer unbalance warning method based on multi-source data fusion - Google Patents
Current transformer unbalance warning method based on multi-source data fusion Download PDFInfo
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Abstract
The invention relates to a current transformer unbalance warning method based on multi-source data fusion.A data acquisition layer acquires online monitoring data, operating data, basic standing account data and environmental data of a current transformer through a RockettMQ, a data processing layer adopts a memory calculation frame, formats the acquired structured data, unstructured data and historical monitoring data, corrects various data according to data influence factors to eliminate invalid data, preprocesses massive historical monitoring data according to an isolated forest algorithm to eliminate abnormal point data, calculates a standard variance, further eliminates data with sample values larger than 2.68 times of variance, calculates an average value to obtain a reference value, calculates the unbalance of each monitoring parameter according to the historical monitoring data and the reference value, and an application layer analyzes the unbalance and unbalance warning factor coefficients to obtain a current transformer unbalance analysis result. The method can improve the accuracy of the analysis of the running state of the current transformer.
Description
Technical Field
The invention relates to the technical field of electric power safety, in particular to a current transformer unbalance degree warning method based on multi-source data fusion.
Background
Since the on-line monitoring service system of the power transformation equipment of the power grid company is built completely, more than ten years of operation data of the power transformation specialty and environment and on-line monitoring of the location of the power grid equipment are accumulated, but due to the lack of fusion utilization of multi-source data and the lack of a means for analyzing the alarm of a current transformer of the power grid, support cannot be provided for the aspects of alarm analysis of the state of the power grid equipment, auxiliary decision making of production management and the like. At present, the online monitoring technology for current transformer equipment is relatively mature, the data accuracy and validity are high, accumulated massive current transformer multi-source data can be fully utilized, the imbalance degree alarm analysis of the current transformer equipment is effectively carried out, the rapid perception of the running state of the current transformer is realized, and the state maintenance work of power grid equipment is carried out on the basis.
In recent years, compared with the traditional data analysis, the data mining analysis based on artificial intelligence presents remarkable advantages, the artificial intelligence data mining technology can synthesize multi-source data, adopts a more efficient intelligent algorithm, and more effectively utilizes the data which is continuously increased and complicated at the present stage. Therefore, the intelligent algorithm and the mass on-line monitoring data, the operation data and the environmental data provide a method means for solving the accurate alarm analysis of the operation state of the current transformer. Various factors related to the running state of the current transformer can be effectively explored through an artificial intelligence data mining technology, and potential rules between the factors and the running state are found. Through the unbalance degree alarm analysis, each professional of the power grid can clearly know the current health condition of the equipment, technical support is provided for developing state maintenance, and the power supply reliability of the power grid is guaranteed.
Disclosure of Invention
In view of this, the present invention provides a current transformer unbalance warning method based on multi-source data fusion, which fuses multi-source data to perform current transformer unbalance analysis, and improves accuracy of current transformer operating state analysis.
The invention is realized by adopting the following scheme: a current transformer unbalance warning method based on multi-source data fusion comprises the following steps:
step S1: acquiring online monitoring data, operation data, basic ledger data and environment data of the current transformer through a RocktMQ message framework;
step S2: adopting a memory computing frame to format the acquired online monitoring data, the operating data, the basic ledger data, the environmental data and the historical monitoring data of the current transformer, correcting various data according to the influence factors of the historical monitoring data, and removing invalid data; preprocessing historical monitoring data according to an isolated forest algorithm to remove abnormal point data; then calculating the standard variance, eliminating data with the sample value being more than 2.68 times of the variance, and calculating to obtain a reference value;
and step S3: calculating the unbalance of the online monitoring data of each current transformer according to the historical monitoring data and the reference value;
and step S4: and analyzing the calculated unbalance degree and the unbalance degree alarm factor coefficients to obtain an unbalance degree alarm analysis result of the current transformer, namely judging whether more than 4 data of nearly 6 unbalance degrees exist and are larger than an unbalance degree threshold value by 0.1, if so, storing detailed data and unbalance degree alarm data, and otherwise, finishing the analysis.
Further, the basic ledger data includes a device name, a model name, an operation date, and a voltage class.
Further, the current transformer online monitoring data comprise leakage current, capacitance and dielectric loss.
Further, in step S2, the correcting various data according to the influence factors of the historical monitoring data, and the specific content of eliminating invalid data is as follows: firstly, judging whether the monitoring data is in a phase failure state, if so, considering the monitoring data as invalid data, otherwise, considering the monitoring data as valid data; and then, according to the monitoring data state judgment rule, determining whether the monitoring data of each monitoring parameter of the monitoring equipment is valid, if the valid data of 10 monitoring data of the monitoring equipment in 10 hours is less than 6, considering that the current data is invalid, and if not, considering that the current data is valid and meets the calculation requirement, and continuing to calculate the reference value.
Further, the specific content of preprocessing the historical monitoring data by using the isolated forest algorithm to remove the abnormal point data in the step S2 is as follows: firstly, creating an isolated tree according to a sample, namely historical monitoring data, and then creating an isolated forest according to the isolated tree; then calculating the abnormal indexes of all samples according to the average heights of the samples on all the isolated trees; and obtaining a class mark according to the abnormal index, setting a normal clustering center and an abnormal clustering center to obtain a training model, finally, predicting and judging the sample participating in calculating the reference value according to the training model, if the prediction result is abnormal, considering the abnormal point to be removed, and otherwise, considering the normal point to be reserved.
Further, the specific process of calculating the standard deviation in step S2 is as follows:
n is the number of samples after eliminating abnormal point data and invalid data, mu is the average value, and x is the average value i Are sample values.
Further, the reference value is calculated in step S2 as: obtaining the sample value at x i -2.68σ＜X i ＜x i Samples in the +2.68 sigma interval, and then calculating the reference value
Further, the calculating of the imbalance degree specifically includes: and respectively enabling A, B and C to be the leakage current, capacitance and dielectric loss hour monitoring data of the current transformer equipment, and calculating the unbalance degree as follows:
ΔBA＝(B-A)/[(B+A+C)/3]；
ΔCB＝(C-B)/[(B+A+C)/3]；
ΔAC＝(A-C)/[(B+A+C)/3]；
delta BA is the resonance deviation proportion between the B phase and the A phase;
delta CB is the same vibration deviation ratio between the phases C and B;
delta AC is the ratio of the co-vibration deviation between A and C phases;
Δ BA original = (B original-a original)/[ (B original + a original + C original)/3 ];
Δ CB original = (C original-B original)/[ (B original + a original + C original)/3 ];
Δ AC original = (a original-C original)/[ (B original + a original + C original)/3 ];
the primary A, the primary B and the primary C are initial values of leakage current, capacitance and dielectric loss of the current transformer respectively;
the initial delta BA is the initial value resonance deviation proportion between the phases B and A;
the initial value of delta CB is the initial value homooscillation deviation ratio between phases C and B;
the initial delta AC is the initial value co-vibration deviation ratio between the phases A and C;
the judgment of delta BA = delta BA-delta BA initial value, which is the difference value of the current data resonance deviation proportion between the phase B and the phase A and the initial value resonance deviation proportion;
the delta CB judgment = delta CB-delta CB initial and is the difference value of the current data co-oscillation deviation ratio between the C phase and the B phase and the initial value co-oscillation deviation ratio;
the delta AC judgment = delta AC-delta AC initial value, which is the difference value of the current data resonance deviation proportion between A and C phases and the initial value resonance deviation proportion;
wherein, the delta BA judgment, the delta CB judgment and the delta AC judgment are final judgment parameters of the current transformer for judgment respectively;
and triggering an alarm when only one data is needed to exceed the unbalance threshold value of 0.1 in the delta BA judgment, the delta CB judgment and the delta AC judgment.
Further, the monitoring data state judgment rule is specifically as follows: when the operation of mother falling exists on the site, the monitoring value is an invalid value, and the front-end device breaks down, overhauls and stops running, the current transformer monitoring data is invalid data.
Compared with the prior art, the invention has the following beneficial effects:
the method disclosed by the invention integrates multi-source data to analyze the unbalance degree of the current transformer, and improves the accuracy of analyzing the running state of the current transformer.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
The invention is further explained by the following embodiments in conjunction with the drawings.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for alarming imbalance of a current transformer based on multi-source data fusion, including the following steps:
step S1: acquiring online monitoring data, operation data, basic ledger data and environment data of the current transformer through a RocktMQ message framework;
step S2: adopting a memory computing frame to format the acquired online monitoring data, the operating data, the basic ledger data, the environmental data and the historical monitoring data of the current transformer, correcting various data according to influence factors of the historical monitoring data, and removing invalid data; preprocessing historical monitoring data according to an isolated forest algorithm to remove abnormal point data; then calculating the standard variance, eliminating data with the sample value being more than 2.68 times of the variance, and calculating to obtain a reference value;
and step S3: calculating the unbalance of the online monitoring data of each current transformer according to the historical monitoring data and the reference value;
and step S4: and analyzing the calculated unbalance and the unbalance alarm factor coefficients to obtain an unbalance alarm analysis result of the current transformer, namely judging whether more than 4 data of almost 6 unbalance are larger than an unbalance threshold value by 0.1, if so, storing detailed data and unbalance alarm data, and otherwise, finishing the analysis.
In this embodiment, the basic ledger data includes a device name, a model name, an operation date, and a voltage class.
In this embodiment, the current transformer online monitoring data includes leakage current, capacitance and dielectric loss.
In this embodiment, first, it is determined whether the monitored data is out of phase, and if so, the monitored data is regarded as invalid data, otherwise, the monitored data is regarded as valid data; and then determining whether the monitoring data of each monitoring parameter of the monitoring equipment is valid or not according to the monitoring data state judgment rule, if the valid data of 10 monitoring data of the monitoring equipment in 10 hours is less than 6, considering that the current data is invalid, and if not, considering that the current data is valid and meets the calculation requirement to continue calculating the reference value.
In this embodiment, the specific content of preprocessing the historical monitoring data by using the isolated forest algorithm to remove the abnormal point data in step S2 is as follows: firstly, creating an isolated tree according to a sample, namely historical monitoring data, and then creating an isolated forest according to the isolated tree; then calculating the abnormal indexes of all samples according to the average heights of the samples on all the isolated trees; and obtaining a class mark according to the abnormal index, setting a normal clustering center and an abnormal clustering center to obtain a training model, finally, predicting and judging the sample participating in calculating the reference value according to the training model, if the prediction result is abnormal, considering the abnormal point to be removed, and otherwise, considering the normal point to be reserved.
In this embodiment, the specific process of calculating the standard deviation in step S2 is:
n is the number of samples after abnormal point data and invalid data are removed, mu is the average value, and x is i Are sample values.
In this embodiment, the reference value in step S2 is calculated as: obtaining the sample value at x i -2.68σ＜X i ＜x i Samples in the +2.68 sigma interval, and then calculating the reference value
In this embodiment, the calculating the imbalance degree specifically includes: let A, B, C be current transformer equipment leakage current, electric capacity, be situated between and decrease hour monitoring data respectively, then the imbalance degree calculation process is as follows:
respectively calculating the same-vibration deviation proportion among leakage current, capacitance and dielectric loss,
ΔBA＝(B-A)/[(B+A+C)/3]；
ΔCB＝(C-B)/[(B+A+C)/3]；
ΔAC＝(A-C)/[(B+A+C)/3]；
delta BA is the same vibration deviation ratio between the B phase and the A phase;
delta CB is the same vibration deviation ratio between the phases C and B;
delta AC is the same vibration deviation ratio between A and C phases;
then according to the A primary, the B primary and the C primary, respectively setting the leakage current, the capacitance and the dielectric loss of the current transformer;
respectively calculating the same vibration deviation proportion among the phases of the leakage current, the capacitance and the dielectric loss reference value,
Δ BA original = (B original-a original)/[ (B original + a original + C original)/3 ];
Δ CB original = (Crim-Boriginal)/[ (Boriginal + Aoriginal + Coriginal)/3 ];
Δ AC original = (a original-C original)/[ (B original + a original + C original)/3 ];
the primary A, the primary B and the primary C are respectively initial values of leakage current, capacitance and dielectric loss of the current transformer;
the initial delta BA is the initial value resonance deviation proportion between the phases B and A;
the initial value of delta CB is the initial value homooscillation deviation ratio between phases C and B;
the initial delta AC is the initial value co-vibration deviation ratio between the phases A and C;
the judgment of delta BA = delta BA-delta BA initial value, which is the difference value of the current data resonance deviation proportion between the phase B and the phase A and the initial value resonance deviation proportion;
the delta CB judgment = delta CB-delta CB initial and is the difference value of the current data same-vibration deviation proportion between the phases C and B and the initial value same-vibration deviation proportion;
the delta AC judgment = delta AC-delta AC initial value, which is the difference value of the current data resonance deviation proportion between A and C phases and the initial value resonance deviation proportion;
wherein, the delta BA judgment, the delta CB judgment and the delta AC judgment are final judgment parameters of the current transformer for judgment respectively;
and triggering an alarm when only one data is needed to exceed the unbalance threshold value of 0.1 in the delta BA judgment, the delta CB judgment and the delta AC judgment.
In this embodiment, the monitoring data state determination rule specifically includes: when the operation of mother falling exists on the site, the monitoring value is an invalid value, and the front-end device breaks down, overhauls and stops running, the current transformer monitoring data is invalid data. In this embodiment, the current transformer monitoring data may be regarded as invalid data when the monitoring value is an invalid value such as-12345.
Preferably, in this embodiment, the data cleansing specifically includes: determining whether the monitoring data of each monitoring parameter of the monitoring equipment is effective or not, and if the effective data of 10 monitoring data of the monitoring equipment in 10 hours is less than 6, rejecting the monitoring equipment; and if the bus bar reversing operation is carried out on the site, the corresponding point data is rejected inefficiently.
The above description is only a preferred embodiment of the present invention, and all the equivalent changes and modifications made according to the claims of the present invention should be covered by the present invention.
Claims (7)
1. A current transformer unbalance degree alarming method based on multi-source data fusion is characterized by comprising the following steps:
the method comprises the following steps:
step S1: acquiring online monitoring data, operation data, basic ledger data and environment data of the current transformer through a RocktMQ message framework;
step S2: adopting a memory computing frame to format the acquired online monitoring data, the operating data, the basic ledger data, the environmental data and the historical monitoring data of the current transformer, correcting various data according to the influence factors of the historical monitoring data, and removing invalid data; preprocessing historical monitoring data according to an isolated forest algorithm to remove abnormal point data; then calculating the standard deviation, eliminating the data with the sample value being more than 2.68 times of the standard deviation, and calculating to obtain a reference value;
and step S3: calculating the unbalance of the online monitoring data of each current transformer according to the historical monitoring data and the reference value;
and step S4: analyzing the calculated imbalance and the imbalance alarm factor coefficients to obtain an imbalance alarm analysis result of the current transformer, namely judging whether more than 4 data of nearly 6 imbalances exist and are larger than an imbalance threshold value by 0.1, if so, storing detailed data and imbalance alarm data, and otherwise, finishing the analysis;
the current transformer on-line monitoring data comprises leakage current, electric capacity and dielectric loss;
the unbalance calculation specifically includes: and respectively enabling A, B and C to be the leakage current, capacitance and dielectric loss hour monitoring data of the current transformer equipment, and calculating the unbalance degree as follows:
ΔBA＝(B-A)/[(B+A+C)/3]；
ΔCB＝(C-B)/[(B+A+C)/3]；
ΔAC＝(A-C)/[(B+A+C)/3]；
delta BA is the resonance deviation proportion between the B phase and the A phase;
delta CB is the same vibration deviation ratio between the phases C and B;
delta AC is the same vibration deviation ratio between A and C phases;
Δ BA original = (B original-a original)/[ (B original + a original + C original)/3 ];
Δ CB original = (C original-B original)/[ (B original + a original + C original)/3 ];
Δ AC original = (Aoriginal-Ci)/[ (Boriginal + Aoriginal + Ci)/3 ];
the primary A, the primary B and the primary C are initial values of leakage current, capacitance and dielectric loss of the current transformer respectively;
the initial delta BA is the initial value resonance deviation proportion between the phases B and A;
the initial value of delta CB is the initial value homooscillation deviation ratio between phases C and B;
the initial delta AC is the initial value co-vibration deviation ratio between the phases A and C;
determining delta BA = delta BA-delta BA initially, and determining the difference value between the co-vibration deviation proportion of the current data between the phase B and the phase A and the initial value co-vibration deviation proportion;
the delta CB judgment = delta CB-delta CB initial and is the difference value of the current data same-vibration deviation proportion between the phases C and B and the initial value same-vibration deviation proportion;
the delta AC judgment = delta AC-delta AC initial value, which is the difference value of the current data resonance deviation proportion between A and C phases and the initial value resonance deviation proportion;
wherein, the delta BA judgment, the delta CB judgment and the delta AC judgment are final judgment parameters of the current transformer for judgment respectively;
and triggering an alarm when only one data is needed to exceed the unbalance threshold value of 0.1 in the delta BA judgment, the delta CB judgment and the delta AC judgment.
2. The multi-source data fusion-based current transformer unbalance warning method according to claim 1, characterized in that: the basic ledger data includes equipment name, model name, commissioning date, and voltage class.
3. The multi-source data fusion-based current transformer unbalance warning method according to claim 1, characterized in that: in the step S2, the correcting of various types of data according to the influence factors of the historical monitoring data, and the specific content of eliminating invalid data is as follows: firstly, judging whether the monitoring data is in a phase failure state, if so, considering the monitoring data as invalid data, otherwise, considering the monitoring data as valid data; and then, according to the monitoring data state judgment rule, determining whether the monitoring data of each monitoring parameter of the monitoring equipment is valid, if the valid data of 10 monitoring data of the monitoring equipment in 10 hours is less than 6, considering that the current data is invalid, and if not, considering that the current data is valid and meets the calculation requirement, and continuing to calculate the reference value.
4. The multi-source data fusion-based current transformer unbalance warning method according to claim 1, characterized in that: the specific content of utilizing the isolated forest algorithm to preprocess the historical monitoring data to remove the abnormal point data in the step S2 is as follows: firstly, creating an isolated tree according to a sample, namely historical monitoring data, and then creating an isolated forest according to the isolated tree; then calculating the abnormal indexes of all samples according to the average heights of the samples on all the isolated trees; and obtaining a class label according to the abnormal index, setting a normal clustering center and an abnormal clustering center to obtain a training model, finally, predicting and judging a sample participating in calculating a reference value according to the training model, if the prediction result is abnormal, considering the sample as an abnormal point, and removing the abnormal point, otherwise, considering the sample as a normal point, and reserving the abnormal point.
5. The current transformer unbalance degree warning method based on multi-source data fusion according to claim 1, characterized in that: the specific process of calculating the standard deviation in the step S2 is as follows:
n is the number of samples after eliminating abnormal point data and invalid data, mu is the average value, and x is the average value i Are sample values.
6. The multi-source data fusion-based current transformer unbalance warning method according to claim 5, characterized in that: the calculation of the reference value in step S2 is: obtaining the sample value at x i -2.68σ＜X i ＜x i Samples in the +2.68 sigma interval, and then calculating the reference value
7. The multi-source data fusion-based current transformer unbalance warning method according to claim 3, characterized in that: the monitoring data state judgment rule is specifically as follows: when the operation of inverting the bus exists on the site, the monitoring value is an invalid value, and the front-end device fails, overhauls and stops running, the current transformer monitoring data is invalid data.
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