CN115586321A - Method, system, memory and equipment for identifying online monitoring data of dissolved gas in oil - Google Patents
Method, system, memory and equipment for identifying online monitoring data of dissolved gas in oil Download PDFInfo
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Abstract
The invention discloses a method, a system, a memory and equipment for identifying on-line monitoring data of dissolved gas in oil, wherein the method takes the mean value and the standard deviation of a time series sample of the dissolved gas in the preprocessed oil, the Euler distance and the standard deviation of the time series sample, and the difference value, the mean value and the standard deviation among the time series samples as statistical characteristics, calculates the upper limit and the lower limit of the statistical characteristic distribution according to the Lauda rule, and constructs a reference sequence of the dissolved gas in the oil; calculating a correlation value between the time sequence to be tested and the reference sequence by utilizing a grey correlation analysis method; and identifying whether the data to be tested is normal or not according to the magnitude of the relevance value. The method can effectively and reasonably process the problem of multi-dimensional data abnormity identification, and provides accurate and reliable basis for the evaluation of the operation state of the oil-immersed transformer and the fault diagnosis.
Description
Technical Field
The invention relates to the technical field of transformer monitoring, in particular to an online monitoring data identification method, system, memory and equipment for dissolved gas in oil.
Background
Analysis of dissolved gas in oil of an oil-immersed transformer is an important means for determining an operation state and a latent fault type, online monitoring data of the dissolved gas in the oil is an important source for monitoring the real-time operation condition of the transformer, and the accuracy and the reliability of the online monitoring data are important guarantees for ensuring the reliable operation of the transformer and a power grid. In actual operation, the online monitoring data is abnormal due to the problems of quality of the online monitoring device, failure of the sensor, abnormality of a data transmission loop and the like. The accurate reliability of the online monitoring data of the dissolved gas in the oil is an important guarantee for guaranteeing the stable operation of transformer equipment and a power system.
At present, a great deal of research has been carried out on the online monitoring data anomaly detection of the oil-immersed transformer as an important link of the transformer state detection. The method has a good identification method for simple abnormal data such as data loss, negative values, overrange and the like; however, there is still room for improvement in the overall identification method for multidimensional data. The traditional mathematical statistics method, the K nearest neighbor method, the random forest and other methods cannot effectively and reasonably solve the problem of abnormal identification of multi-dimensional data.
Disclosure of Invention
The invention aims to provide an oil dissolved gas online monitoring data identification method, system, memory and equipment, which solve the problem that the existing method cannot effectively and reasonably identify multi-dimensional data abnormity.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention provides an online monitoring data identification method for dissolved gas in oil, which comprises the following steps:
constructing a dissolved gas reference sequence in oil based on the multi-dimensional statistical characteristics of the dissolved gas in oil of the oil-immersed transformer on-line monitoring data time sequence sample;
calculating a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
and identifying the state of the monitoring data of the to-be-detected dissolved gas according to the correlation value.
Further, the multidimensional statistical characteristics of the time series samples of the online monitoring data of the dissolved gas in the oil immersed transformer oil comprise:
determining a time window based on an online monitoring sampling period of dissolved gas in oil of the oil-immersed transformer, and establishing an online monitoring data time sequence sample set of the dissolved gas in the oil according to a time sequence;
reconstructing the time series sample set of the online monitoring data of the dissolved gas in the oil by adopting square root transformation to the time series sample set of the online monitoring data of the dissolved gas in the oil;
calculating the mean value and standard deviation of the characteristic gas corresponding to all time sequences based on the reconstructed time sequence sample set of the online monitoring data of the dissolved gas in the oil;
calculating the Euler distance mean value and the corresponding standard deviation between each time series sample and the mean value of the characteristic gas corresponding to all the sequences based on the reconstructed time series sample set of the online monitoring data of the dissolved gas in the oil;
calculating the difference value sequence mean value and the corresponding standard deviation of adjacent time sequence samples based on the reconstructed online monitoring data time sequence sample set of the dissolved gas in the oil;
and forming the multidimensional statistical characteristic of the dissolved gas online monitoring data time series samples in the oil based on the mean value of the characteristic gases corresponding to all the sequences, the standard deviation of the characteristic gases corresponding to all the sequences, the Euler distance mean value between the mean values of the characteristic gases corresponding to all the sequences, the standard deviation between the mean values of the characteristic gases corresponding to all the sequences, the difference value sequence mean value of adjacent time series samples and the standard deviation corresponding to the difference value sequence mean value of adjacent time series samples.
Further, the method for constructing the reference sequence of dissolved gas in oil comprises the following steps:
establishing an upper limit and a lower limit of a normal value of the online monitoring data of the dissolved gas in the oil based on the Lauda rule;
establishing an upper limit and a lower limit of an abnormal value of online monitoring data of the dissolved gas in the oil based on Lauda rule;
and taking the time sequence of each calculated mean value, the upper limit and the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as a dissolved gas reference sequence in the oil.
Further, the calculating a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and the reference sequence of the dissolved gas in the oil comprises:
establishing a grey correlation analysis model facing the mean value of the time sequence by taking the mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of the characteristic gas corresponding to all the sequences as reference sequences;
establishing a grey correlation analysis model facing the Euler distances among the time sequences by taking the Euler distance mean, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as reference sequences;
establishing a grey correlation analysis model facing the difference value between the time sequences by taking the difference value sequence mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of adjacent time sequence samples as reference sequences;
sequentially substituting the solution gas monitoring data to be detected into the 3 established grey correlation analysis models, and calculating the correlation between the solution gas monitoring data to be detected and the reference sequence to obtain the grey correlation sequence and the correlation value of each model;
the monitoring data of the dissolved gas to be detected is that the dissolved gas in the oil at a certain moment: monitoring data for hydrogen, methane, ethane, ethylene and acetylene.
Further, identifying the state of the monitoring data of the solution gas to be detected according to the relevance value comprises the following steps:
determining the state corresponding to the maximum value in each grey correlation degree sequence, wherein the states comprise normal and abnormal states;
and if the states corresponding to the at least two maximum values are normal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is normal, and if the states corresponding to the at least two maximum values are abnormal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is abnormal.
The invention provides a system for identifying abnormal data of online monitoring of dissolved gas in oil of an oil-immersed transformer, which comprises:
the reference sequence building module is used for building a dissolved gas reference sequence in oil based on the multi-dimensional statistical characteristics of the dissolved gas online monitoring data time sequence sample in the oil immersed transformer oil;
the correlation degree calculation module is used for calculating a correlation degree value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
and the identification module is used for identifying the state of the monitoring data of the dissolved gas to be detected according to the correlation value.
Further, the method also comprises the following steps:
the system comprises an original data acquisition module, a time window determination module and a data processing module, wherein the original data acquisition module is used for determining a time window based on an online monitoring sampling period of dissolved gas in oil of the oil-immersed transformer and establishing an online monitoring data time sequence sample set of the dissolved gas in the oil according to a time sequence;
reconstructing the time series sample set of the online monitoring data of the dissolved gas in the oil by adopting square root transformation to the time series sample set of the online monitoring data of the dissolved gas in the oil;
and the number of the first and second groups,
the characteristic calculation module is used for calculating the mean value and the standard deviation of characteristic gas corresponding to all sequences based on the reconstructed time sequence sample set of the online monitoring data of the dissolved gas in the oil;
calculating Euler distance mean values and corresponding standard deviations between the time series samples and mean values of characteristic gases corresponding to all the sequences based on the reconstructed online monitoring data time series sample set of the dissolved gas in the oil;
calculating the mean value of the difference sequence of the adjacent time sequence samples and the corresponding standard deviation based on the reconstructed time sequence sample set of the online monitoring data of the dissolved gas in the oil;
and forming the multidimensional statistical characteristic of the dissolved gas online monitoring data time series samples in the oil based on the mean value of the characteristic gases corresponding to all the sequences, the standard deviation of the characteristic gases corresponding to all the sequences, the Euler distance mean value between the mean values of the characteristic gases corresponding to all the sequences, the standard deviation between the mean values of the characteristic gases corresponding to all the sequences, the difference value sequence mean value of adjacent time series samples and the standard deviation corresponding to the difference value sequence mean value of adjacent time series samples.
Further, the reference sequence construction module is specifically configured to,
establishing an upper limit and a lower limit of a normal value of the online monitoring data of the dissolved gas in the oil based on the Lauda rule;
establishing an upper limit and a lower limit of an abnormal value of online monitoring data of the dissolved gas in the oil based on Lauda rule;
and taking the time sequence of each calculated mean value, the upper limit and the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as a dissolved gas reference sequence in the oil.
Further, the relevancy calculation module is specifically configured to,
establishing a grey correlation analysis model facing the mean value of the time sequence by taking the mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of the characteristic gas corresponding to all the sequences as reference sequences;
establishing a grey correlation analysis model facing the Euler distances among the time sequences by taking the Euler distance mean value, the normal value upper limit, the normal value lower limit and the abnormal value upper limit and lower limit as reference sequences;
taking the mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of the adjacent time sequence sample difference value sequence as reference sequences, and establishing a grey correlation analysis model facing the difference value between the time sequences;
sequentially substituting the dissolved gas monitoring data to be detected into the 3 established grey correlation analysis models, and calculating the correlation between the dissolved gas monitoring data to be detected and the reference sequence to obtain the grey correlation sequence and the correlation value of each model;
the monitoring data of the dissolved gas to be detected is that the dissolved gas in the oil at a certain moment: monitoring data for hydrogen, methane, ethane, ethylene and acetylene.
Further, the identification module is specifically configured to,
determining the state corresponding to the maximum value in each grey correlation degree sequence, wherein the states comprise normal and abnormal states;
and if the states corresponding to the at least two maximum values are normal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is normal, and if the states corresponding to the at least two maximum values are abnormal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is abnormal.
A third aspect of the invention provides a computer readable memory storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a method according to any of the foregoing methods.
A fourth aspect of the invention provides an apparatus comprising,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the foregoing methods.
The invention has the following beneficial effects:
the invention provides a method for identifying the abnormality of online monitoring data of dissolved gas in oil of an oil-immersed transformer. The invention establishes multidimensional statistical characteristics, takes the characteristics as the correlation between reference calculation and data to be detected, realizes effective and reasonable processing of the problem of multi-dimensional data abnormity identification, and provides accurate and reliable basis for oil-immersed transformer operation state evaluation and fault diagnosis.
Drawings
Fig. 1 is a flowchart of an online monitoring data identification method for dissolved gas in oil according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and 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 invention.
Example 1
The embodiment provides an online monitoring data anomaly identification method for dissolved gas in oil of an oil-immersed transformer, which comprises the following steps:
constructing a dissolved gas reference sequence in oil based on the multi-dimensional statistical characteristics of the dissolved gas in oil of the oil-immersed transformer on-line monitoring data time sequence sample;
calculating a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
and identifying the state of the monitoring data of the to-be-detected dissolved gas according to the correlation value.
In this embodiment, the dissolved gas in the oil immersed transformer oil is composed of content characteristics of 5 characteristic gases, including: hydrogen, methane, ethane, ethylene and acetylene.
In this embodiment, the multidimensional statistical characteristics of the time series samples of the online monitoring data of the dissolved gas in the oil include a mean value and a standard deviation, a euler distance and a standard deviation of a sample time series, a difference value of adjacent time series samples, a mean value of a difference sequence, and a standard deviation.
In this embodiment, the state of the monitoring data of the dissolved gas to be detected in the oil is identified according to the maximum correlation criterion.
Example 2
The embodiment provides a method for identifying online monitoring data of dissolved gas in oil of an oil-immersed transformer, and the method is specifically implemented in the following steps with reference to fig. 1:
step1: acquiring a time sequence sample of online monitoring data of dissolved gas in oil pretreated by the oil-immersed transformer;
step2: calculating multidimensional statistical characteristics based on the time series samples of the online monitoring data of the dissolved gas in the oil;
step3: constructing a dissolved gas reference sequence in the oil based on the calculated multi-dimensional statistical characteristics;
step4: calculating a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
step5: and identifying the state of the monitoring data of the dissolved gas to be detected in the oil according to the maximum correlation criterion.
In this embodiment, a time series sample of online monitoring data of dissolved gas in oil pretreated by an oil immersed transformer is obtained, and the specific implementation process is as follows:
step11, online monitoring data time sequence samples of dissolved gas in oil of the oil-immersed transformer are composed of content characteristics of 5 characteristic gases; the 5 characteristic gases include hydrogen, methane, ethane, ethylene and acetylene.
Step12, determining a time window based on an online monitoring sampling period of the dissolved gas in the oil pretreated by the oil-immersed transformer, and establishing an online monitoring data time sequence sample set T of the dissolved gas in the oil according to a time sequence;
wherein T represents an online monitoring data time sequence sample set of the dissolved gas in the oil with the time window length of n, T i I =1,2, …, n denotes the sample of the on-line monitoring data of the dissolved gas in the oil at the moment before the ith sampling interval, H 2,i I =1,2, …, n represents the online monitoring data sample of hydrogen in oil at the moment before the ith sampling interval, CH 4,i I =1,2, …, n denotes the sample of the online monitoring data of methane in oil at the time before the ith sampling interval, C 2 H 6i And i =1,2 and …, n represents an online ethane monitoring data sample in oil at the moment before the ith sampling interval, C 2 H 4i And i =1,2 and …, n represents an online monitoring data sample of ethylene in oil at the time before the ith sampling interval, C 2 H 2,i I =1,2, …, n represents an online acetylene monitoring data sample in oil at a time before the ith sampling interval;
step13, reconstructing the time series set T of the online monitoring data of the dissolved gas in the oil by adopting square root transformation to the time series sample set T of the online monitoring data of the dissolved gas in the oil SQ ：
T SQi I =1,2, …, n represents the square root of the on-line monitored data sample of dissolved gas in oil at the time before the ith sampling interval.
The purpose of this step is to improve the data contained in the time series set T to be close to or fit with the normal distribution.
In this embodiment, the multidimensional statistical characteristics of the time series samples of the dissolved gas in the oil are calculated, and the specific implementation process is as follows:
the statistical characteristics comprise the average value and the standard deviation, the Euler distance and the standard deviation of the sample time sequence, the difference value of samples of adjacent time sequences, the average value and the standard deviation of the difference sequence and the like;
step21, on-line monitoring data time series sample set T based on reconstructed dissolved gas in oil SQ Calculating the mean value E of all the series corresponding characteristic gases cs And standard deviation sigma cs The calculation is as follows:
step22, on-line monitoring data time sequence sample set T based on reconstructed dissolved gas in oil SQ Calculating the average value E of the characteristic gas corresponding to each time series sample and all the series cs Mean euler distance E between OL And corresponding standard deviation sigma OL The calculation is as follows:
step23, on-line monitoring data time series sample set T based on reconstructed dissolved gas in oil SQ Calculating the difference absolute value of adjacent time sequence samples and the difference sequence mean value E CZ And corresponding standard deviation σ CZ The calculation formula is as follows:
T CZ,i ＝|T SQi -T SQ(i+1) |；
wherein, T CZ,i Represents the on-line monitoring data of the dissolved gas in the oil at the moment before the ith sampling intervalThe samples differ in absolute value from adjacent sample intervals.
In this embodiment, a dissolved gas reference sequence in oil is constructed based on the multidimensional statistical characteristics of the time series samples of the dissolved gas in oil, and the specific implementation process is as follows:
step31, establishing the upper limit and the lower limit of the normal value of the online monitoring data of the dissolved gas in the oil based on Lauda law, wherein E csup3 、E OLup3 And E CZup3 Respectively represent the mean values E of the sample sequences of dissolved gases in oil within the time window of the sequences cs Upper limit of normal value, average value E of time series samples and corresponding characteristic gas of all the series cs Mean euler distance E between OL Upper limit of normal value and time series sample set T SQ Mean value E of sequence of adjacent time differences CZ Upper limit of normal value; e cslow3 、E OLlow3 And E CZlow3 Respectively represent the mean values E of the sample sequences of dissolved gases in oil within the time window of the sequences cs Average value E of normal value lower limit, time series samples and corresponding characteristic gas of all sequences cs Euler distance mean E therebetween OL Lower normal value limit and time series sample set T SQ Mean value E of sequence of adjacent time differences CZ Lower limit of normal value;
E csup3 ＝E cs +3×σ cs ；E cslow3 ＝E cs -3×σ cs ；
E OLup3 ＝E OL +3×σ OL ；E OLlow3 ＝E OL -3×σ OL ；
E CZup3 ＝E CZ +3×σ CZ ；E CZlow3 ＝E CZ -3×σ CZ ；
step32, establishing an upper limit and a lower limit of an abnormal value of the online monitoring data of the dissolved gas in the oil based on Lauda rule: wherein E csup4 、E OLup4 And E CZup4 Respectively represent mean values E of the sequences of samples of dissolved gas in oil within a time window of the sequences cs Upper limit of abnormal value, mean value E of time series samples and characteristic gas corresponding to all sequences cs Mean euler distance E between OL Upper limit of abnormal valueAnd a set of time series samples T SQ Mean value E of sequence of adjacent time differences CZ An upper outlier limit; e cslow4 、E OLlow4 And E CZlow4 Respectively represent the mean values E of the sample sequences of dissolved gases in oil within the time window of the sequences cs Abnormal value lower limit, time series sample and mean value E of characteristic gas corresponding to all sequences cs Euler distance mean E therebetween OL Outlier floor and time series sample set T SQ Mean value E of sequence of adjacent time differences CZ A lower outlier limit;
E cscup4 ＝E cs +4×σ cs ；E csclow4 ＝E cs -4×σ cs ；
E OLCup4 ＝E OL +4×σ OL ；E OLClow4 ＝E OL -4×σ OL ；
E CZCup4 ＝E CZ +4×σ CZ ；E CZClow4 ＝E CZ -4×σ CZ ；
step33, taking the mean value of the solved time series, the upper and lower limits of the normal value and the time series of the upper and lower limits of the abnormal value as a reference sequence S of the dissolved gas in the oil ref 。
In this embodiment, a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil is calculated, and the specific implementation process is as follows:
step41, mean value E cs Upper limit of normal value E csup3 Lower limit of normal value E cslow3 And an upper limit E of abnormal value cscup4 And a lower limit E csclow4 As a reference sequence, establishing a grey correlation analysis model facing to a time sequence mean value; by mean value E OL Upper limit of normal value E OLup3 Lower limit of normal value E OLlow3 And an upper limit E of abnormal value OLCcup4 And a lower limit E OLClow4 As a reference sequence, establishing a grey correlation analysis model facing the Euler distance between time sequences; by mean value E CZ Upper limit of normal value E CZup3 Lower limit of normal value E CZlow3 And an upper limit E of abnormal value CZCcup4 And a lower limit E CZClow4 As a referenceEstablishing a grey correlation analysis model facing to the difference value between the time sequences;
step42, sequentially substituting the solution gas monitoring data to be detected into the 3 established gray correlation analysis models, and calculating the correlation between the solution gas monitoring data to be detected and the reference sequence to obtain the gray correlation sequence of each model:
wherein D is cs 、D csup3 、D cslow3 、D cscup4 And D cslow4 Respectively monitoring data and E of the solution gas to be detected cs 、E csup3 、E cslow3 E cscup4 And E csclow4 Of correlation value, D OL 、D OLup3 、D OLlow3 、D OLcup4 And D OLlow4 Respectively monitoring data and E of the solution gas to be detected OL 、E OLup3 、E OLlow3 E OLCcup4 And E OLClow4 Correlation value of D CZ 、D CZup3 、D CZlow3 、D CZcup4 And D CZlow4 Respectively monitoring data and E of the solution gas to be detected CZ 、E CZup3 、E CZlow3 E CZCcup4 And E CZClow4 The correlation value of (2).
It should be noted that the solution gas monitoring data to be detected is oil solution gas monitoring data at a certain time, and at the time t = n + i, the expression is as follows:
S test,n+i ＝[H 2(n+i) ,CH 4(n+i) ,C 2 H 6(n+i) ,C 2 H 4(n+i) ,C 2 H 2(n+i) ]。
in this embodiment, the state of the monitoring data of the dissolved gas to be detected in the oil is identified according to the maximum correlation criterion, and the specific implementation process is as follows:
determining the maximum value Max (D) in each grey relevance sequence 1 )、Max(D 2 ) And Max (D) 3 ) Corresponding state, wherein D cs 、D OL And E OL The corresponding state is normal; d csup3 、D OLup3 And D CZup3 The corresponding state is normal; d cslow3 、D OLlow3 And D CZlow3 The corresponding state is normal; d cscup4 、D OLcup4 And D CZcup4 The corresponding state is abnormal; d cslow4 、D OLlow4 And D CZlow4 The corresponding state is abnormal;
and if the states corresponding to the at least two maximum values are normal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as normal, and if the states corresponding to the at least two maximum values are abnormal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as abnormal.
In the embodiment, the method comprises the steps of establishing multi-dimensional statistical characteristics by adopting the mean value, the Euler distance and the difference value of adjacent time series of dissolved gas time series samples in oil, and determining the upper and lower limits of normal values and abnormal values of all dimensional characteristics by utilizing a Lauda rule so as to establish a reference sequence; and then establishing grey correlation analysis to determine the correlation degree between the time sequence to be measured and the reference sequence, so as to effectively and reasonably process the problem of multi-dimensional data abnormity identification, and provide accurate and reliable data for the evaluation of the operation state of the oil-immersed transformer and the fault diagnosis.
Example 3
This embodiment provides a solution gas on-line monitoring data identification system in oil, includes:
the reference sequence building module is used for building a dissolved gas reference sequence in oil based on the multidimensional statistical characteristics of the time sequence sample of the online monitoring data of the dissolved gas in the oil of the oil-immersed transformer;
the correlation degree calculation module is used for calculating the correlation degree value between the monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and the reference sequence of the dissolved gas in the oil;
and the identification module is used for identifying the state of the monitoring data of the dissolved gas to be detected according to the correlation value.
In this embodiment, the method further includes:
the original data acquisition module is used for determining a time window based on an online monitoring sampling period of the dissolved gas in the oil of the oil-immersed transformer, and establishing an online monitoring data time sequence sample set of the dissolved gas in the oil according to a time sequence:
wherein T represents an online monitoring data time sequence sample set of the dissolved gas in the oil with the time window length of n, T i I =1,2, …, n denotes the sample of the on-line monitoring data of the dissolved gas in the oil at the moment before the ith sampling interval, H 2,i I =1,2, …, n represents the online monitoring data sample of hydrogen in oil at the moment before the ith sampling interval, CH 4,i I =1,2, …, n denotes the sample of the online monitoring data of methane in oil at the time before the ith sampling interval, C 2 H 6i And, i =1,2, …, n represents the on-line monitoring data sample of ethane in oil at the time before the ith sampling interval, C 2 H 4i, I =1,2, …, n denotes the sample of the on-line monitoring data of ethylene in oil at the time before the ith sampling interval, C 2 H 2,i I =1,2, …, n represents an online acetylene monitoring data sample in oil at a time before the ith sampling interval;
and (3) performing square root transformation on the time series sample set T of the online monitoring data of the dissolved gas in the oil, and reconstructing the time series sample set of the online monitoring data of the dissolved gas in the oil as follows:
wherein, T SQ A time series sample set of on-line monitoring data for the reconstructed dissolved gas in oil, T SQi I =1,2, …, n represents the square root of the online monitoring data sample of dissolved gas in oil at the time before the ith sampling interval;
and the number of the first and second groups,
a characteristic calculation module for on-line monitoring data time series sample set T based on the reconstructed oil dissolved gas SQ Calculating the mean value E of all the series corresponding characteristic gases cs And standard deviation sigma cs The following are:
on-line monitoring data time series sample set T based on reconstructed dissolved gas in oil SQ Calculating the mean value E of each time series sample and the characteristic gas corresponding to all the series cs Mean euler distance E between OL And corresponding standard deviation sigma OL The following are:
on-line monitoring data time series sample set T based on reconstructed dissolved gas in oil SQ Calculating the difference value sequence mean value E of the adjacent time sequence samples CZ And corresponding standard deviation σ CZ The following are:
T CZ,i ＝|T SQi -T SQ(i+1) |；
wherein, T CZ,i Representing the absolute value of the difference between the online monitoring data sample of the dissolved gas in the oil and the sample of the adjacent sampling interval at the moment before the ith sampling interval;
will E cs 、σ cs 、E OL 、σ OL 、E CZ And σ CZ And forming the multidimensional statistical characteristics of the time series samples of the online monitoring data of the dissolved gas in the oil.
In this embodiment, the reference sequence construction module is specifically configured to,
based on Lauda's rule, establishing the upper limit and the lower limit of the normal value of the online monitoring data of the dissolved gas in the oil as follows:
E csup3 ＝E cs +3×σ cs ；E cslow3 ＝E cs -3×σ cs ；
E OLup3 ＝E OL +3×σ OL ；E OLlow3 ＝E OL -3×σ OL ；
E CZup3 ＝E CZ +3×σ CZ ；E CZlow3 ＝E CZ -3×σ CZ ；
wherein E is csup3 、E OLup3 And E CZup3 Respectively represent the mean values E cs Upper normal value, mean E OL Upper limit of normal value and mean value E CZ Upper limit of normal value; e cslow3 、E OLlow3 And E CZlow3 Respectively represent the mean values E cs Lower normal value, mean E OL Lower normal value and mean E CZ Lower limit of normal value;
based on Lauda's rule, establishing the upper limit and the lower limit of abnormal values of the online monitoring data of the dissolved gas in the oil, as follows:
E cscup4 ＝E cs +4×σ cs ；E csclow4 ＝E cs -4×σ cs ；
E OLCup4 ＝E OL +4×σ OL ；E OLClow4 ＝E OL -4×σ OL ；
E CZCup4 ＝E CZ +4×σ CZ ；E CZClow4 ＝E CZ -4×σ CZ ；
wherein E is csup4 、E OLup4 And E CZup4 Respectively represent the mean values E cs Upper limit of abnormal value, mean E OL Upper limit of outlier and mean E CZ An outlier ceiling; e cslow4 、E OLlow4 And E CZlow4 Respectively represent the mean values E cs Lower limit of abnormal value, mean E OL Lower outlier limit and mean E CZ A lower outlier limit;
and taking the time sequence of each calculated mean value, the upper limit and the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as a dissolved gas reference sequence in the oil.
In this embodiment, the association degree calculating module is specifically configured to,
by mean value E cs Upper limit of normal value E csup3 Lower limit of normal value E cslow3 And an upper limit E of abnormal value cscup4 And a lower limit E csclow4 As a reference sequence, establishing a grey correlation analysis model facing to a time sequence mean value; by mean value E OL Upper limit of normal value E OLup3 Lower limit of normal value E OLlow3 And an upper limit E of abnormal value OLCcup4 And a lower limit E OLClow4 As a reference sequence, establishing a grey correlation analysis model facing the Euler distance between time sequences; by mean value E CZ Upper limit of normal value E CZup3 Lower limit of normal value E CZlow3 And an upper limit E of abnormal value CZCcup4 And a lower limit E CZClow4 As a reference sequence, establishing a grey correlation analysis model facing the difference between the time sequences;
and sequentially substituting the solution gas monitoring data to be detected into the 3 established grey correlation analysis models, and calculating the correlation between the solution gas monitoring data to be detected and the reference sequence to obtain the grey correlation sequence of each model:
wherein D is a grey correlation value matrix, D 1 、D 2 And D 3 As a sequence of grey correlation values, D cs 、D csup3 、D cslow3 、D cscup4 And D cslow4 Respectively monitoring data and E of the solution gas to be detected cs 、E csup3 、E cslow3 E cscup4 And E csclow4 Correlation value of D OL 、D OLup3 、D OLlow3 、D OLcup4 And D OLlow4 Respectively monitoring data and E of the solution gas to be detected OL 、E OLup3 、E OLlow3 E OLCcup4 And E OLClow4 Correlation value of D CZ 、D CZup3 、D CZlow3 、D CZcup4 And D CZlow4 Respectively monitoring data and E of the solution gas to be detected CZ 、E CZup3 、E CZlow3 E CZCcup4 And E CZClow4 The correlation value of (2). (ii) a
The monitoring data of the dissolved gas to be detected is that the dissolved gas in the oil at a certain moment: monitoring data for hydrogen, methane, ethane, ethylene and acetylene.
In this embodiment, the identification module is specifically configured to,
determining the maximum value Max (D) in each grey correlation sequence 1 )、Max(D 2 ) And Max (D) 3 ) Corresponding state, wherein D cs 、D OL And E OL The corresponding state is normal; d csup3 、D OLup3 And D CZup3 The corresponding state is normal; d cslow3 、D OLlow3 And D CZlow3 The corresponding state is normal; d cscup4 、D OLcup4 And D CZcup4 The corresponding state is abnormal; d cslow4 、D OLlow4 And D CZlow4 The corresponding state is abnormal;
and if the states corresponding to the at least two maximum values are normal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as normal, and if the states corresponding to the at least two maximum values are abnormal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as abnormal.
Example 4
The present embodiments provide a computer readable memory storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of embodiment 1 or embodiment 2.
Example 5
The present embodiment provides an apparatus comprising, in combination,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method according to any of embodiments 1 or 2.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, 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 and/or block diagram block or blocks.
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 and/or block diagram block or blocks.
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 and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. The method for identifying the on-line monitoring data of the dissolved gas in the oil is characterized by comprising the following steps:
constructing a dissolved gas reference sequence in oil based on the multi-dimensional statistical characteristics of the online monitoring data time sequence sample of the dissolved gas in the oil of the oil-immersed transformer;
calculating a correlation value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
and identifying the state of the monitoring data of the to-be-detected dissolved gas according to the correlation value.
2. The method for identifying the on-line monitoring data of the dissolved gas in the oil according to claim 1, wherein the multidimensional statistical characteristics of the time-series samples of the on-line monitoring data of the dissolved gas in the oil of the oil-immersed transformer comprise:
determining a time window based on an online monitoring sampling period of dissolved gas in oil of the oil-immersed transformer, and establishing an online monitoring data time sequence sample set of the dissolved gas in the oil according to a time sequence;
reconstructing the time series sample set of the online monitoring data of the dissolved gas in the oil by adopting square root transformation to the time series sample set of the online monitoring data of the dissolved gas in the oil;
calculating the mean value and standard deviation of the characteristic gas corresponding to all time series based on the reconstructed time series sample set of the online monitoring data of the dissolved gas in the oil;
calculating the Euler distance mean value and the corresponding standard deviation between each time series sample and the mean value of the characteristic gas corresponding to all the sequences based on the reconstructed time series sample set of the online monitoring data of the dissolved gas in the oil;
calculating the difference value sequence mean value and the corresponding standard deviation of adjacent time sequence samples based on the reconstructed online monitoring data time sequence sample set of the dissolved gas in the oil;
and forming the multidimensional statistical characteristic of the dissolved gas online monitoring data time series samples in the oil based on the mean value of the characteristic gases corresponding to all the sequences, the standard deviation of the characteristic gases corresponding to all the sequences, the Euler distance mean value between the mean values of the characteristic gases corresponding to all the sequences, the standard deviation between the mean values of the characteristic gases corresponding to all the sequences, the difference value sequence mean value of adjacent time series samples and the standard deviation corresponding to the difference value sequence mean value of adjacent time series samples.
3. The method for identifying the data on-line monitoring of the dissolved gas in the oil as claimed in claim 2, wherein the constructing of the reference sequence of the dissolved gas in the oil comprises:
establishing an upper limit and a lower limit of a normal value of the online monitoring data of the dissolved gas in the oil based on the Lauda rule;
establishing an upper limit and a lower limit of an abnormal value of online monitoring data of the dissolved gas in the oil based on Lauda rule;
and taking the calculated mean value, the upper limit and the lower limit of the normal value and the time sequence of the upper limit and the lower limit of the abnormal value as a dissolved gas reference sequence in oil.
4. The method for identifying the on-line monitoring data of the dissolved gas in the oil according to claim 3, wherein the calculating of the correlation value between the monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and the reference sequence of the dissolved gas in the oil comprises:
establishing a grey correlation analysis model facing the mean value of the time sequence by taking the mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of the characteristic gas corresponding to all the sequences as reference sequences;
establishing a grey correlation analysis model facing the Euler distances among the time sequences by taking the Euler distance mean, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as reference sequences;
establishing a grey correlation analysis model facing the difference value between the time sequences by taking the difference value sequence mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of adjacent time sequence samples as reference sequences;
sequentially substituting the solution gas monitoring data to be detected into the 3 established grey correlation analysis models, and calculating the correlation between the solution gas monitoring data to be detected and the reference sequence to obtain the grey correlation sequence and the correlation value of each model;
the monitoring data of the dissolved gas to be detected is that the dissolved gas in the oil at a certain moment: monitoring data for hydrogen, methane, ethane, ethylene and acetylene.
5. The method for identifying the on-line monitoring data of the dissolved gas in the oil as claimed in claim 4, wherein identifying the state of the monitoring data of the dissolved gas to be detected according to the correlation value comprises:
determining the state corresponding to the maximum value in each grey correlation degree sequence, wherein the states comprise normal and abnormal states;
and if the states corresponding to the at least two maximum values are normal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is normal, and if the states corresponding to the at least two maximum values are abnormal, identifying that the state of the monitoring data of the dissolved gas to be detected in the oil is abnormal.
6. Solution gas on-line monitoring data identification system in oil, its characterized in that includes:
the reference sequence building module is used for building a dissolved gas reference sequence in oil based on the multidimensional statistical characteristics of the time sequence sample of the online monitoring data of the dissolved gas in the oil of the oil-immersed transformer;
the correlation degree calculation module is used for calculating a correlation degree value between monitoring data of the dissolved gas to be detected in the oil of the oil-immersed transformer and a reference sequence of the dissolved gas in the oil;
and the identification module is used for identifying the state of the monitoring data of the dissolved gas to be detected according to the correlation value.
7. The system for on-line monitoring and data identification of dissolved gas in oil as claimed in claim 6, further comprising:
the system comprises an original data acquisition module, a time sequence acquisition module and a data processing module, wherein the original data acquisition module is used for determining a time window based on an online monitoring sampling period of dissolved gas in oil of the oil-immersed transformer and establishing an online monitoring data time sequence sample set of the dissolved gas in the oil according to a time sequence;
reconstructing the time series sample set of the online monitoring data of the dissolved gas in the oil by adopting square root transformation to the time series sample set of the online monitoring data of the dissolved gas in the oil;
and (c) a second step of,
the characteristic calculation module is used for calculating the mean value and the standard deviation of characteristic gas corresponding to all sequences based on the reconstructed time sequence sample set of the online monitoring data of the dissolved gas in the oil;
calculating Euler distance mean values and corresponding standard deviations between the time series samples and mean values of characteristic gases corresponding to all the sequences based on the reconstructed online monitoring data time series sample set of the dissolved gas in the oil;
calculating the mean value of the difference sequence of the adjacent time sequence samples and the corresponding standard deviation based on the reconstructed time sequence sample set of the online monitoring data of the dissolved gas in the oil;
and forming the multidimensional statistical characteristic of the dissolved gas online monitoring data time series samples in the oil based on the mean value of the characteristic gases corresponding to all the sequences, the standard deviation of the characteristic gases corresponding to all the sequences, the Euler distance mean value between the mean values of the characteristic gases corresponding to all the sequences, the standard deviation between the mean values of the characteristic gases corresponding to all the sequences, the difference value sequence mean value of adjacent time series samples and the standard deviation corresponding to the difference value sequence mean value of adjacent time series samples.
8. The system for on-line monitoring and data identification of dissolved gas in oil according to claim 7, wherein the reference sequence construction module is specifically configured to,
establishing an upper limit and a lower limit of a normal value of the online monitoring data of the dissolved gas in the oil based on the Lauda rule;
establishing an upper limit and a lower limit of an abnormal value of online monitoring data of the dissolved gas in the oil based on Lauda rule;
and taking the time sequence of each calculated mean value, the upper limit and the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as a dissolved gas reference sequence in the oil.
9. The system for on-line monitoring and data identification of dissolved gas in oil according to claim 8, wherein the correlation calculation module is specifically configured to,
establishing a grey correlation analysis model facing the mean value of the time sequence by taking the mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of the characteristic gas corresponding to all the sequences as reference sequences;
establishing a grey correlation analysis model facing the Euler distances among the time sequences by taking the Euler distance mean, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value as reference sequences;
establishing a grey correlation analysis model facing the difference value between the time sequences by taking the difference value sequence mean value, the upper limit of the normal value, the lower limit of the normal value and the upper limit and the lower limit of the abnormal value of adjacent time sequence samples as reference sequences;
sequentially substituting the solution gas monitoring data to be detected into the 3 established grey correlation analysis models, and calculating the correlation between the solution gas monitoring data to be detected and the reference sequence to obtain the grey correlation sequence and the correlation value of each model;
the monitoring data of the dissolved gas to be detected is that the dissolved gas in the oil at a certain moment: monitoring data for hydrogen, methane, ethane, ethylene and acetylene.
10. The system for on-line monitoring and data identification of dissolved gas in oil as claimed in claim 9, wherein the identification module is specifically configured to,
determining the state corresponding to the maximum value in each grey correlation degree sequence, wherein the states comprise normal and abnormal states;
and if the states corresponding to the at least two maximum values are normal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as normal, and if the states corresponding to the at least two maximum values are abnormal, identifying the state of the monitoring data of the dissolved gas to be detected in the oil as abnormal.
11. A computer readable memory storing one or more programs, wherein: the one or more programs include instructions that, when executed by a computing device, cause the computing device to perform any of the methods of claims 1-5.
12. An apparatus, characterized by: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-5.
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