阅读说明：本技术 一种变压器油中溶解气体异常检测方法和系统 (Method and system for detecting abnormity of dissolved gas in transformer oil ) 是由 王宏刚 纪鑫 褚娟 葛鑫亮 武同心 赵晓龙 董林啸 李建芳 何禹德 于 2021-07-16 设计创作，主要内容包括：本发明提供了一种变压器油中溶解气体异常检测方法和系统,包括：获取变压器油中溶解的各气体的监测数据集中的气体含量,并根据所述各气体含量进行速率特征提取,得到各气体的产气速率；通过孤立森林算法分别对所述各气体产气速率和各气体含量进行异常初检,得到各气体的异常得分；对所述各气体的异常得分进行聚类,并根据聚类结果、各气体的异常得分、各气体的产气速率和各气体含量进行气体的异常终检,得到变压器油中溶解的各气体异常终检结果。本发明能有效避免数据特征的“维度灾难”,且计算速度快；同时考虑气体含量和气体速率变化情况,并设置异常得分阈值,更易捕捉潜在异常；通过赋予不同窗口时间,有效判断变压器油中溶解气体是否异常。(The invention provides a method and a system for detecting the abnormity of dissolved gas in transformer oil, which comprises the following steps: acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristics according to the gas content to obtain the gas production rate of each gas; respectively carrying out abnormity initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain an abnormity score of each gas; and clustering the abnormal scores of the gases, and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain abnormal final inspection results of the gases dissolved in the transformer oil. The invention can effectively avoid the dimension disaster of the data characteristics and has high calculation speed; meanwhile, the gas content and the gas speed change condition are considered, and an abnormity score threshold value is set, so that potential abnormity can be captured more easily; by giving different window time, whether the dissolved gas in the transformer oil is abnormal or not is effectively judged.)

1. A method for detecting the abnormality of dissolved gas in transformer oil is characterized by comprising the following steps:

acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristics according to the gas content to obtain the gas production rate of each gas;

respectively carrying out abnormity initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain an abnormity score of each gas;

and clustering the abnormal scores of the gases, and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain abnormal final inspection results of the gases dissolved in the transformer oil.

2. The method as claimed in claim 1, wherein the obtaining of the abnormal score of each gas by performing the abnormal initial inspection of the gas production rate and the gas content of each gas through the isolated forest algorithm comprises:

respectively constructing each gas sample set by taking the gas production rate and the gas content of each gas as characteristics;

extracting sample subsets according to the gas sample sets, and constructing and training a gas characteristic isolation tree by using the sample subsets respectively;

and calculating the abnormal score of each sample in each subset as the abnormal score of the corresponding gas according to the structure of each trained gas feature isolated tree.

3. The method of claim 2, wherein the anomaly score for the samples in the subset is calculated as:

in the formula (I), the compound is shown in the specification,is the anomaly score for sample X, X being the sample in sample subset X, E [ h (X)]Representing the expectation of the path length of sample x over a plurality of isolated trees of gas characteristics,representing the number of samples in each gas characteristic isolation tree of the sample subset X,for indicatingThe average path length of the gas characteristic isolated tree constructed by the strip samples;

wherein is made ofAverage path length of isolated tree of gas features constructed by strip samplesCalculated as follows:

in the formula (I), the compound is shown in the specification,to sum the sums.

4. The method of claim 1, wherein the clustering the anomaly scores of the gases and performing anomaly final inspection on the gases according to the clustering result, the anomaly scores of the gases, the gas production rate of the gases and the content of the gases to obtain the anomaly final inspection result of each gas dissolved in the transformer oil comprises:

clustering the abnormal scores of the gases by a K-Means clustering method to obtain a clustering result of the abnormal scores of the gases;

and performing gas abnormity final inspection on the abnormity scores of the gases, the gas production rate of the gases and the content of the gases according to the distribution condition of the abnormity scores of the gases of each category in the clustering result and based on the set content early warning value, gas production rate early warning value and set abnormity score threshold value of each oil to obtain abnormity final inspection results of each gas dissolved in the transformer oil.

5. The method according to claim 4, wherein the step of performing the abnormal final detection of the gas on the abnormal score of each gas, the gas production rate of each gas and the content of each gas based on the set content warning value, the set gas production rate warning value and the set abnormal score threshold value of each gas dissolved in each oil according to the distribution of the abnormal scores of each category of gas in the clustering result to obtain the abnormal final detection result of each gas dissolved in the transformer oil comprises the following steps:

according to the distribution condition of the abnormal scores of the gases in each category in the clustering result, based on the preset content early warning value and gas production rate early warning value of the dissolved gas in each oil, marking the gas production rate of each gas exceeding the gas production rate early warning value or the clustering category where the content of each gas exceeding the gas content early warning value is positioned as abnormal clustering;

based on the set early warning value of the dissolved gas in each oil and the set abnormal score threshold value, marking the gas production rate and the gas content of the gas, the gas production rate and the gas content of which have the abnormal score within the set abnormal score threshold value but have the gas production rate and the gas content of which have the abnormal score not exceeding the gas production rate early warning value and the gas content of which do not exceed the gas content early warning value, as attention values;

and taking the abnormal clustering and the attention value as the final detection result of the gas abnormality dissolved in the transformer oil.

6. The method of claim 1, wherein the gas generation rate is calculated according to the following formula:

in the formula, V is the gas production rate, delta y is the variation of the gas content, delta t is the time interval, m is the total oil quantity of the equipment, and rho is the oil density;

wherein, the time interval Δ t is calculated according to the following formula:

in the formula, t_iIn order to monitor the time of day,at the moment of minimum gas content, y_iIs t_iGas content value, y, monitored at any time_i-daysIs at t_iMinimum gas content value in days before the moment, day is a variable time parameter.

7. The method of claim 1, wherein prior to obtaining the monitored data set for each gas dissolved in the transformer oil, further comprising:

acquiring online monitoring data of dissolved gas in transformer oil and transformer standing book data, and correlating;

grouping the correlated data according to the coding and phase combination of the transformer to obtain monitoring data of the dissolved gas in the transformer oil of each group;

and cleaning data of the monitoring data of the dissolved gas in the transformer oil of each group.

8. The method of claim 7, wherein the data cleaning of the monitoring data of dissolved gas in the transformer oil of each group comprises:

based on the set upper limit threshold and lower limit threshold of the dissolved gas in each group of transformer oil, deleting the monitoring data larger than the upper limit threshold and the monitoring data smaller than the lower limit threshold;

deleting all 0 monitoring data in the monitoring data of the dissolved gas in the transformer oil of each group;

keeping the first monitoring data in the continuously repeated monitoring data in the monitoring data of the dissolved gas in the transformer oil of each group;

and deleting the monitoring data which are out of the measuring range of the instrument in the monitoring data of the dissolved gas in the transformer oil of each group.

9. A system for detecting the abnormality of dissolved gas in transformer oil is characterized by comprising: the system comprises a rate characteristic extraction module, an abnormity initial detection module and an abnormity final detection module;

the rate characteristic extraction module is used for acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristic according to the gas content to obtain the gas production rate of each gas;

the abnormal initial detection module is used for respectively carrying out abnormal initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain abnormal scores of each gas;

and the abnormal final inspection module is used for clustering the abnormal scores of the gases and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain the abnormal final inspection result of the gases dissolved in the transformer oil.

10. The system of claim 9, wherein the anomaly final detection module comprises: a clustering unit and a final inspection unit;

the clustering unit is used for clustering the abnormal scores of the gases by a K-Means clustering method to obtain clustering results of the abnormal scores of the gases;

and the final inspection unit is used for performing gas abnormal final inspection on the abnormal scores of the gases, the gas production rate of the gases and the content of the gases according to the distribution condition of the abnormal scores of the gases in each category in the clustering result and based on the set content early warning value and gas production rate early warning value of the gases in each oil and the set abnormal score threshold value, so as to obtain the abnormal final inspection result of each gas dissolved in the transformer oil.

Technical Field

The invention belongs to the field of anomaly detection of oil-immersed transformers, and particularly relates to a method and a system for detecting anomaly of dissolved gas in transformer oil.

Background

The large power transformer is important power transformation equipment of a power system, the running state of the large power transformer is important for the safety and stability of a power grid, and the online monitoring and real-time diagnosis of the transformer state are very important. For a long time, the power equipment maintenance strategy mainly adopts the regular maintenance taking time as a standard. Although regular maintenance generally can find the defects of the equipment during maintenance, the method plays an important role in ensuring the safe and economic operation of the equipment. However, the periodic maintenance may not be timely, which may lead to a decrease in the reliability of the equipment. The analysis technology of the dissolved gas in the transformer oil is an important method for diagnosing the power transformer, and can effectively find latent faults and development degrees inside the transformer.

At present, the analysis of dissolved gas in transformer oil is mainly classified into a statistical analysis method represented by a fixed threshold value and an IEC three-ratio value, and an artificial intelligence method represented by a neural network and a covariance determinant. Wherein the fixed threshold value depends on expert experience, and historical trend analysis of gas and potential faults which do not exceed the threshold value are not considered, and the abnormal detection result is directly influenced. The artificial intelligence method has the defects that the artificial intelligence method is influenced by sample sets and time characteristics, and when the samples are selected improperly or data are continuously lost and fluctuated, the identification effect is poor; secondly, the change characteristic of the gas velocity is not considered, only the gas content is analyzed, and the characteristic is single; and the final detection time is longer, and when the detection sample size is larger, the identification time is longer. Therefore, a method for effectively detecting the dissolved gas in the transformer oil is urgently needed.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a method for detecting the abnormality of dissolved gas in transformer oil, which comprises the following steps:

acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristics according to the gas content to obtain the gas production rate of each gas;

respectively carrying out abnormity initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain an abnormity score of each gas;

and clustering the abnormal scores of the gases, and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain abnormal final inspection results of the gases dissolved in the transformer oil.

Preferably, the obtaining of the abnormal score of each gas by respectively performing the abnormal initial detection on the gas production rate and the gas content of each gas through the isolated forest algorithm comprises:

respectively constructing each gas sample set by taking the gas production rate and the gas content of each gas as characteristics;

extracting sample subsets according to the gas sample sets, and constructing and training a gas characteristic isolation tree by using the sample subsets respectively;

and calculating the abnormal score of each sample in each subset as the abnormal score of the corresponding gas according to the structure of each trained gas feature isolated tree.

Preferably, the anomaly score of the samples in the subset is calculated as follows:

in the formula (I), the compound is shown in the specification,is the anomaly score for sample X, X being the sample in sample subset X, E [ h (X)]Representing the expectation of the path length of sample x over a plurality of isolated trees of gas characteristics,representing the number of samples in each gas characteristic isolation tree of the sample subset X,for indicatingThe average path length of the gas characteristic isolated tree constructed by the strip samples;

wherein is made ofAverage path length of isolated tree of gas features constructed by strip samplesCalculated as follows:

in the formula (I), the compound is shown in the specification,to sum the sums.

Preferably, the clustering the abnormal scores of the gases and performing the abnormal final inspection of the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain the abnormal final inspection result of the gases dissolved in the transformer oil includes:

clustering the abnormal scores of the gases by a K-Means clustering method to obtain a clustering result of the abnormal scores of the gases;

and performing gas abnormity final inspection on the abnormity scores of the gases, the gas production rate of the gases and the content of the gases according to the distribution condition of the abnormity scores of the gases of each category in the clustering result and based on the set content early warning value, gas production rate early warning value and set abnormity score threshold value of each oil to obtain abnormity final inspection results of each gas dissolved in the transformer oil.

Preferably, the performing, according to the distribution of the abnormal scores of the gases in each category in the clustering result, the abnormal final inspection of the gases on the abnormal score of each gas, the gas production rate of each gas, and the content of each gas based on the set content warning value, gas production rate warning value, and set abnormal score threshold value of each gas dissolved in each oil to obtain the abnormal final inspection result of each gas dissolved in the transformer oil includes:

according to the distribution condition of the abnormal scores of the gases in each category in the clustering result, based on the preset content early warning value and gas production rate early warning value of the dissolved gas in each oil, marking the gas production rate of each gas exceeding the gas production rate early warning value or the clustering category where the content of each gas exceeding the gas content early warning value is positioned as abnormal clustering;

based on the set early warning value of the dissolved gas in each oil and the set abnormal score threshold value, marking the gas production rate and the gas content of the gas, the gas production rate and the gas content of which have the abnormal score within the set abnormal score threshold value but have the gas production rate and the gas content of which have the abnormal score not exceeding the gas production rate early warning value and the gas content of which do not exceed the gas content early warning value, as attention values;

and taking the abnormal clustering and the attention value as the final detection result of the gas abnormality dissolved in the transformer oil.

Preferably, the gas generation rate is calculated according to the following formula:

in the formula, V is the gas production rate, delta y is the variation of the gas content, delta t is the time interval, m is the total oil quantity of the equipment, and rho is the oil density;

wherein, the time interval Δ t is calculated according to the following formula:

in the formula, t_iIn order to monitor the time of day,at the moment of minimum gas content, y_iIs t_iGas content value, y, monitored at any time_i-daysIs at t_iMinimum gas content value in days before the moment, day is a variable time parameter.

Preferably, before the acquiring the monitoring data set of each gas dissolved in the transformer oil, the method further includes:

acquiring online monitoring data of dissolved gas in transformer oil and transformer standing book data, and correlating;

grouping the correlated data according to the coding and phase combination of the transformer to obtain monitoring data of the dissolved gas in the transformer oil of each group;

and cleaning data of the monitoring data of the dissolved gas in the transformer oil of each group.

Preferably, the data cleaning of the monitoring data of the dissolved gas in the transformer oil of each group includes:

based on the set upper limit threshold and lower limit threshold of the dissolved gas in each group of transformer oil, deleting the monitoring data larger than the upper limit threshold and the monitoring data smaller than the lower limit threshold;

deleting all 0 monitoring data in the monitoring data of the dissolved gas in the transformer oil of each group;

keeping the first monitoring data in the continuously repeated monitoring data in the monitoring data of the dissolved gas in the transformer oil of each group;

and deleting the monitoring data which are out of the measuring range of the instrument in the monitoring data of the dissolved gas in the transformer oil of each group.

Based on the same invention concept, the invention also provides a system for detecting the abnormity of the dissolved gas in the transformer oil, which comprises the following components: the system comprises a rate characteristic extraction module, an abnormity initial detection module and an abnormity final detection module;

the rate characteristic extraction module is used for acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristic according to the gas content to obtain the gas production rate of each gas;

the abnormal initial detection module is used for respectively carrying out abnormal initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain abnormal scores of each gas;

and the abnormal final inspection module is used for clustering the abnormal scores of the gases and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain the abnormal final inspection result of the gases dissolved in the transformer oil.

Preferably, the abnormality final inspection module includes: a clustering unit and a final inspection unit;

the clustering unit is used for clustering the abnormal scores of the gases by a K-Means clustering method to obtain clustering results of the abnormal scores of the gases;

and the final inspection unit is used for performing gas abnormal final inspection on the abnormal scores of the gases, the gas production rate of the gases and the content of the gases according to the distribution condition of the abnormal scores of the gases in each category in the clustering result and based on the set content early warning value and gas production rate early warning value of the gases in each oil and the set abnormal score threshold value, so as to obtain the abnormal final inspection result of each gas dissolved in the transformer oil.

Compared with the closest prior art, the invention has the following beneficial effects:

the invention provides a method and a system for detecting the abnormity of dissolved gas in transformer oil, which comprises the following steps: acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristics according to the gas content to obtain the gas production rate of each gas; respectively carrying out abnormity initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain an abnormity score of each gas; and clustering the abnormal scores of the gases, and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain abnormal final inspection results of the gases dissolved in the transformer oil. The invention can effectively avoid the dimension disaster of the data characteristics by the isolated forest algorithm and has high calculation speed; meanwhile, the gas content and the gas speed change condition are considered, and potential abnormality is easier to capture by setting an abnormality score threshold; and obtaining a plurality of detection results by giving different window time, and effectively judging whether the dissolved gas in the transformer oil is abnormal or not.

The invention relates to the online monitoring data of the dissolved gas in the transformer oil and the transformer standing book data, and fully analyzes the historical trend of the gas.

Drawings

FIG. 1 is a schematic flow chart of a method for detecting an abnormality of a dissolved gas in transformer oil according to the present invention;

FIG. 2 is a general flow chart of an embodiment of the present invention for analyzing the anomaly of dissolved gas in transformer oil;

FIG. 3 is a flow chart of data preprocessing of an embodiment of analysis of an anomaly of dissolved gases in transformer oil according to the present invention;

FIG. 4 is a schematic diagram of an anomaly preliminary detection flow of an embodiment of analyzing anomalies in dissolved gas in transformer oil according to the present invention;

FIG. 5 is a flow chart of an anomaly preliminary detection method for analyzing anomalies in dissolved gas in transformer oil according to an embodiment of the present invention;

FIG. 6 is a flow chart of the final abnormal inspection of an embodiment of the analysis of the abnormality of the dissolved gas in the transformer oil according to the present invention;

fig. 7 is a schematic diagram of a basic structure of a system for detecting an abnormality of a dissolved gas in transformer oil according to the present invention.

Detailed Description

The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.

Example 1:

the flow schematic diagram of the method for detecting the abnormity of the dissolved gas in the transformer oil is shown in figure 1, and the method comprises the following steps:

step 1: acquiring the gas content in the monitoring data set of each gas dissolved in the transformer oil, and extracting the rate characteristics according to the gas content to obtain the gas production rate of each gas;

step 2: respectively carrying out abnormity initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain an abnormity score of each gas;

and step 3: and clustering the abnormal scores of the gases, and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain abnormal final inspection results of the gases dissolved in the transformer oil.

Before the step 1, on-line monitoring data of dissolved gas in the transformer oil and transformer standing book data need to be acquired and correlated; grouping the associated monitoring data according to the ID and phase combination of the transformer; and cleaning the grouped monitoring data to eliminate abnormal distribution data.

The data cleaning is carried out on the grouped monitoring data, and abnormal distribution data are eliminated, and the method comprises the following two aspects: statistical method-based data cleansing and rule-based data cleansing.

The data cleaning based on the statistical method comprises the following steps:

a1. counting three-phase monitoring data amounts of dissolved gas in different transformer oils, giving an upper limit threshold value and a lower limit threshold value aiming at the data amount monitored by each phase, and deleting corresponding data if the data amount is larger than the upper limit threshold value or smaller than the lower limit threshold value and the data amount is considered to be abnormal in the transmission process;

a2. and analyzing the stability of the monitoring device by calculating the coefficient of variation of the cleaned gas content monitoring data, and if the gas content monitoring data exceeds a threshold value, determining that the acquisition device is unstable and deleting corresponding data.

The data cleaning based on the rule comprises the following steps:

b1. the data of all gas contents are 0 value: the online monitoring device cannot generate the condition that the gas content is 0, if the gas content is 0, the sensor is considered to be abnormal, and corresponding data are deleted;

b2. each gas content data is a continuously repeated case: if the gas content of each online monitoring device continuously has the same value, the sensor is considered to be abnormal, and after the online monitoring device is verified with the defect record, the defect record is found to have an alarm record of the gas content abnormal value, so that the gas content abnormal value cannot be directly deleted, if continuous repetition exists, only the earliest collected data is selected, and other abnormal samples are deleted;

b3. the content data of each gas is out of the range of the meter: if the monitoring data has a value less than 0 or is data of type 999999, the corresponding data is deleted if the metering range of the meter is exceeded.

The rate feature extraction in the step 1 specifically includes:

the research provides a rate characteristic calculation strategy, taking one gas as an example, and the gas production rates of other gases are calculated in the same way, which is specifically described as follows:

given a variable time days parameter, the preprocessed monitor data set D { (t)_i,y_i),i＝1,2,……,N}，t_iTo monitor time, y_iTo monitor the gas content values.

Selected gas base content y₀Current time content y_iSelecting the minimum gas content in days before the current time as a base period, wherein the change delta y of the gas content can be expressed as:

Δy＝argmin(y_i,y_i-days)

the time interval Δ t is calculated as the time change by the difference between the current time and the minimum content time, i.e. the time change is

In the formula, t_iIn order to monitor the time of day,at the minimum content of gasAnd (6) engraving.

The absolute gas production rate V of each gas is expressed as:

wherein m is the total oil quantity of the equipment, and rho is the oil density.

The step 2 specifically comprises the following steps:

respectively constructing each gas sample set by taking the gas production rate and the gas content of each gas as characteristics;

and extracting sample subsets according to the gas sample sets, and constructing and training a gas characteristic isolation tree by using the sample subsets respectively. In this embodiment, the gas characteristic isolation tree is also called an isolation tree or binary tree.

(1) Sample set X { (l) constructed from gas content and rate₁,v₁),(l₂,v₂),……(l_n,v_n) And randomly drawing psi sample points to form a subset X of X, wherein l_iMonitoring of the content for gases, v_iThe gas production rate of the gas at the same time is N dimensions in total;

(2) randomly assigning a dimension q from the N characteristics, randomly generating a cutting point p in the current data, and satisfying the following conditions:

min(x_ij,j＝q,x_ij∈x)＜p＜max(x_ij,j＝q,x_ij∈x)；

(3) cutting the sample space into two subspaces, and setting sample points with the dimensionality smaller than p to be placed into a left child node and setting sample points with the dimensionality larger than or equal to p to be placed into a right child node;

(4) repeating the steps (2) and (3) to ensure that all leaf nodes have only one sample point or the isolated tree (iTree) reaches the specified height until t gas characteristic isolated trees (iTree) are generated;

repeating the steps until the following conditions are met: the data is not re-divisible (i.e. contains only one piece of data) or the data is identical or binary tree throughout to a defined maximum depth. At this point, the binary tree training is complete.

And calculating the abnormal score of each sample in each subset as the abnormal score of the corresponding gas according to the structure of each trained gas feature isolated tree.

The anomaly score for a sample in the subset is calculated as:

in the formula (I), the compound is shown in the specification,is the anomaly score for sample X, X being the sample in sample subset X, E [ h (X)]Representing the expectation of the path length of sample x over a plurality of isolated trees of gas characteristics,representing the number of samples in each gas characteristic isolation tree of the sample subset X,for indicatingThe average path length of the gas characteristic isolated tree constructed by the strip samples;

wherein is made ofAverage path length of isolated tree of gas features constructed by strip samplesCalculated as follows:

in the formula (I), the compound is shown in the specification,to sum the sums.

The step 3 specifically comprises the following steps:

and clustering the abnormal scores of the gases by a K-Means clustering method to obtain a clustering result of the abnormal scores of the gases.

And (3) clustering analysis is carried out on abnormal results by adopting K-Means, unsupervised classification is carried out on abnormal characteristics, abnormal samples of each group are analyzed, and detection and screening of abnormal values are further realized. The specific process is as follows:

and (a) merging and recombining the detected abnormalities of each group for normalization treatment, and eliminating dimensional influence.

Mapping the original data to a range of [0, 1] by a maximum and minimum normalization method, wherein the formula is as follows:

step (b), the default initial K value belongs to [2, m ], K belongs to integers, the contour coefficient is calculated, and the optimal K value is determined;

randomly selecting K data points from the abnormal data set as a centroid;

step (d), calculating the distance (such as Euclidean distance) between each point in the data set and each centroid, and dividing the point to which the centroid belongs when the point is close to which centroid;

dividing all data into sets, wherein K sets exist in total, and then recalculating the centroid of each set;

step (f), if the distance between the newly-calculated centroid and the original centroid is smaller than the convergence requirement of the algorithm, the clustering can be considered to reach the expected result, and the algorithm is terminated;

and (g) if the distance between the new centroid and the original centroid does not meet the convergence requirement, iterating the steps d-f.

And performing gas abnormity final inspection on the abnormity scores of the gases, the gas production rate of the gases and the content of the gases according to the distribution condition of the abnormity scores of the gases of each category in the clustering result and based on the set content early warning value, gas production rate early warning value and set abnormity score threshold value of each oil to obtain abnormity final inspection results of each gas dissolved in the transformer oil.

According to the distribution condition of the abnormal scores of the gases in each category in the clustering result, based on the preset content early warning value and gas production rate early warning value of the dissolved gas in each oil, marking the gas production rate of each gas exceeding the gas production rate early warning value or the clustering category where the content of each gas exceeding the gas content early warning value is positioned as abnormal clustering;

based on the set early warning value of the dissolved gas in each oil and the set abnormal score threshold value, marking the gas production rate and the gas content of the gas, the gas production rate and the gas content of which have the abnormal score within the set abnormal score threshold value but have the gas production rate and the gas content of which have the abnormal score not exceeding the gas production rate early warning value and the gas content of which do not exceed the gas content early warning value, as attention values;

and taking the abnormal clustering and the attention value as the final detection result of the gas abnormality dissolved in the transformer oil.

The invention provides a method and a system for detecting the abnormality of dissolved gas in transformer oil, which are used for monitoring and early warning whether the running state of a transformer is stable or not and whether the health state of equipment is good or not, and are shown in a flow chart 2.

The anomaly detection can be implemented in three stages, specifically as follows:

in the first stage, the invention provides a full-dimensional anomaly identification method, which introduces windowed gas production rate characteristics to find potential and suspected anomalies.

And in the second stage, a preliminary high-efficiency detection method is provided, and the preliminary abnormal identification is realized based on an isolated forest algorithm.

And in the third stage, an anomaly detection method is provided, and a detection idea from preliminary screening to a final result is provided. And further detecting the abnormality through the abnormal score probability distribution after clustering, and screening final abnormal data.

The invention also has the advantages that:

1. the method of the invention is different from the previous distance similarity calculation, effectively avoids the 'dimension disaster' of the data characteristics, has high calculation speed, can perform distributed deployment and is better suitable for the environment of the current big data information technology.

2. The invention expands the analysis scope of the gas dissolved in the oil, not only considers the gas content, but also analyzes the change condition of the gas speed, and is easier to capture potential abnormality.

3. The invention provides a flexible window threshold, and multiple detection results are obtained by giving different window time, and are effectively compared with multiple offline experiments to judge whether the running state of the transformer is abnormal or not.

Example 2:

the technical scheme is clearly and completely described in the following by combining the drawings and the embodiment. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

The identification of the abnormity of the dissolved gas in the oil is explained by three parts, namely data preprocessing, initial identification of the abnormity of the dissolved gas in the oil and final screening of the abnormity.

Data cleaning is an important part of the invention, and the accuracy of the detection model is influenced by the cleaning degree. The invention eliminates the possible abnormity of the monitoring device and the transmission process, carries out abnormity detection of dissolved gas in oil based on the eliminated data, and specifically explains and explains the abnormal cleaning as follows:

the specific flow of the data preprocessing method of the present invention is shown in fig. 3, and includes the following two contents:

firstly, according to different environments and operation parameters of transformers, the transformers are grouped based on a combination mode of 'transformer ID + phase difference', and missing values are filled by mode.

Secondly, the invention further cleans the abnormity on the basis of combined filling, eliminates irrelevant abnormity affecting a detection model by a 'rule + statistic' method, such as abnormity of a monitoring device, abnormity of transmission and the like, ensures that a sample for analyzing the abnormity of the dissolved gas in the oil belongs to abnormal data generated by the operation of the transformer, and comprises the following two contents:

(1) statistical analysis, which is mainly based on a descriptive statistical analysis method (statistics and statistical distribution), and identifies the monitoring data abnormity:

counting three-phase monitoring data amounts of dissolved gas in different transformer oils, giving an upper limit threshold value and a lower limit threshold value aiming at the data amount monitored by each phase, and considering that the data are abnormal in the transmission process if the data amounts are larger than the upper limit threshold value or smaller than the lower limit threshold value;

analyzing the stability of the monitoring device by calculating the coefficient of variation of the content monitoring data of the phase-to-phase gas after cleaning, and if the content monitoring data exceeds a threshold value, determining that the acquisition device is unstable;

the statistical analysis also includes fluctuation degree detection and singular value detection. The fluctuation degree detection judges abnormality by judging the relationship between the monitoring data and a light threshold value, and when the fluctuation degree is greater than the light threshold value, the data is abnormal. Singular value detection determines data anomalies by screening for outliers.

(2) The business rules are mainly summarized and summarized aiming at the abnormal problems of the monitoring and metering device:

the monitoring data of each gas content shows that all the gas contents are 0 value: the online monitoring device cannot generate the condition that the gas content is 0, and if the gas content is 0, the sensor is considered to be abnormal;

all the gas contents of the monitoring data of the gas contents are continuously collected and repeated: if the gas content of each online monitoring device continuously has the same value, the sensor is considered to be abnormal, the online monitoring device is verified with a defect record, and the defect record is found to have an alarm record with the gas content abnormal value, so that the defect record cannot be directly deleted;

each gas content monitoring data exceeds the meter condition: if the monitoring data has a value less than 0 or is data of type "999999", the meter metering range is considered to be exceeded.

And finally, integrating the statistical analysis result and the business rule analysis result, deleting the set sample data from the sample data, and finishing data cleaning.

The invention adopts an isolated forest detection method in an oil dissolved gas abnormity detection model, and considers that the gas rate is usually calculated to assist fault judgment in an offline experiment of the content of the dissolved gas in the oil by actual business, so that the gas production rate characteristic is introduced. The gas production rate calculation formula is as follows:

c_jmeasuring a certain gas concentration in the oil for a second sampling, c_iFor the first sampling a certain gas concentration in the oil is measured, Δ t is the actual running time (day) in the subsampling time interval, m is the total oil quantity of the plant, and ρ is the density of the oil.

And integrating the gas content and the gas production rate characteristic set, and identifying abnormal values through an Isolation Forest algorithm. Not only is the abnormal condition of the gas content in the oil identified, but also the abnormal condition of the gas content rate can be found.

And clustering the abnormal sets through K-Means for the preliminarily identified abnormal sets.

And analyzing the clustering group, marking an early warning value and an attention value by giving a threshold value socres, and taking the early warning value and the attention value as final abnormality detection.

The invention discloses a method for identifying abnormality of dissolved gas in oil, wherein a schematic diagram of the general structure of the initial abnormality detection is shown in figure 4.

Fig. 5 is a flow chart of an anomaly initial detection method for analyzing anomalies in dissolved gas in transformer oil, which specifically includes the following steps:

and step 01, windowing absolute gas production rate characteristic.

Given a variable time days parameter, the preprocessed monitor data set D { (t)_i,y_i),i＝1,2,……,N}，t_iTo monitor time, y_iTo monitor the gas content values.

Selected gas base content y₀Content y at present_iThe selection strategy is: minimum gas content in days before the present timeAs the base period, the change Δ y of the gas content can be expressed as:

Δy＝argmin(y_i,y_i-days)

calculating the time interval, the difference between the current time and the minimum content time, as the time change, i.e.

In the formula, t_iIn order to monitor the time of day,the moment of minimum gas content.

Calculating the absolute gas production rate of the gas, and expressing the absolute gas production rate as:

wherein m is the total oil quantity of the transformer, and rho is the density of the transformer oil.

And step 02, carrying out primary identification on the abnormity.

And carrying out preliminary anomaly identification on the dissolved gas in the oil by adopting an isolation forest (island forest) algorithm.

The isolated forest algorithm is a rapid outlier detection method based on Ensemble, has linear time complexity and high precision, and is a State-of-the-art algorithm which meets the requirement of big data processing. The algorithm isolates samples using a binary search tree structure called the isolation tree iTree.

In the embodiment of the invention, an isolated forest algorithm is used for carrying out abnormal recognition and differentiation on characteristic information of gas content and gas rate, and the specific characteristics are hydrogen (H2), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), total hydrocarbon, carbon monoxide absolute gas production rate, hydrogen absolute gas production rate, carbon dioxide absolute gas production rate, methane absolute gas production rate, acetylene absolute gas production rate, ethylene absolute gas production rate, ethane absolute gas production rate and total hydrocarbon absolute rate.

Optionally, the method provided by the invention adopts an isolated forest to perform anomaly analysis on the dissolved gas in the oil, and identifies the abnormal state of the transformer gas at the abnormal moment, wherein the identification process may include: according to the constructed content and rate characteristics, firstly, a certain characteristic is randomly selected to construct an isolated tree (iTree), secondly, the growth of each isolated tree is trained, thirdly, the height of each sample point is integrated through a tree structure, and finally, the abnormal score of each sample is calculated according to the path length.

In an example, the content and rate characteristic information after integration comprises M characteristics, and the method adopts an isolated forest algorithm to perform abnormal preliminary identification. The process may include the following steps a1 through D1. The specific steps and processes are as follows:

and step A1, integrating the characteristic information as model input.

And (4) associating and combining the gas content of the transformer and the windowed gas production rate through monitoring time and the ID field of the transformer, and constructing a sample data set X.

X＝{(l₁,v₁),(l₂,v₂),……(l_n,v_n)}，l_iMonitoring of the content for gases, v_iAnd in order to simultaneously generate the gas rate, unsupervised machine learning is carried out through the constructed sample training set.

And step B1, constructing an isolated tree.

StepB1-1, for dataset X, randomly extracts ψ sample points from X to make a subset X of X to put in the root node.

StepB1-2 randomly designates a dimension q from M dimensions, and randomly generates a cut point p in the current data (the cut point is generated between the maximum value and the minimum value of the designated dimension in the current node data), i.e. the cut point p is generated

min(x_ij,j＝q,x_ij∈x)＜p＜max(x_ij,j＝q,x_ij∈x)

StepB1-3, the cut point p generates a hyperplane, and divides the current data space into two subspaces: sample points with dimensions smaller than p are designated to be placed in the left child node, and sample points with dimensions larger than or equal to p are designated to be placed in the right child node.

And C1, training the growth of the isolated tree.

The height limit of the tree is related to the number of subsamples psi. The height of the tree is limited because we are concerned only with points with shorter path lengths, which are more likely to be outliers, and not with normal points where the path is very long.

StepC1-1, StepB1-2 and StepB1-3 in recursive step B1, until all leaf nodes have had only one sample point or the orphan Tree (iTree) has reached a specified height.

StepC1-2, cycling StepB1-1 in step B1, StepB1-2, StepB1-3 and StepC1-1 in step C1 until t orphan trees (iTrees) were generated.

Repeating the steps until the following conditions are met: the data is not re-divisible, namely only one piece of data is contained, or all the data are the same; the binary tree reaches a defined maximum depth.

And D1, predicting the abnormity.

The algorithm belongs to unsupervised learning, and needs to calculate the abnormal score of the data X and judge whether the sample belongs to an abnormal point.

StepD1-1, estimate the path length of the sample in each iTree.

StepD1-1-1, firstly along an iTree, starting from a root node and according to values of different characteristics, from top to bottom until reaching a certain leaf node.

StepD1-1-2, assuming that the number of samples falling on the leaf node where x is located in the training sample of the iTree is T.size, the path length h (x) of the sample x on the iTree is

h(x)＝e+C(T.size)

In the formula, e represents the number of edges that sample data x passes from the root node of the iTree to the leaf node, and C (t.size) can be considered as a correction value representing the average path length in a binary tree constructed from t.size strip sample data. In general, the formula for C (n) is as follows:

where H (n-1) can be estimated as ln (n-1) +0.5772156649, where the constants are Euler constants.

StepD1-2, calculating the sample abnormality score.

Data x final anomaly score (x) combines the results of several itrees.

In the formula, E [ h (x)]Represents the mean of the path lengths of the data x over the plurality of itrees,the number of samples representing a training sample of a single iTree,for indicatingThe average path length of the binary tree constructed by the data is mainly used for normalization.

And step 03, finally identifying the abnormity.

In the abnormal initial identification result set, the normal sample points are classified into the abnormal information, so that the abnormal set needs to be further screened, and the abnormal final inspection process is shown in fig. 6.

Optionally, the method of the present invention adopts K-Means clustering and statistical threshold combination for anomaly screening to distinguish an abnormal value from a normal value, and the process includes: firstly, giving a range of an initial class K instead of a fixed value, determining a K value by iteratively calculating a contour coefficient evaluation index, and then dividing an abnormal set by a clustering algorithm; and finally, combining the abnormal groups of each category by setting a sorcs threshold value to form a final abnormal result.

The method comprises the following steps of taking the characteristics of a primary abnormal result set X, such as content, rate, abnormal score value and the like as input information, and realizing group classification through a clustering algorithm, wherein the specific steps are as follows:

step A2, clustering the abnormal data, analyzing the class characteristics, the clustering process includes the following steps:

StepA2-1, merging and recombining the detected abnormalities in each group for normalization treatment, and eliminating dimensional influence.

Mapping the original data to a range of [0, 1] by a maximum and minimum normalization method, wherein the formula is as follows:

StepA2-2, the default initial K value belongs to [2, m ], K belongs to an integer, the contour coefficient is calculated, and the optimal K value is determined.

StepA2-3, randomly selecting K data points from the outlier dataset as centroids.

StepA2-4, calculates the distance (e.g., euclidean distance) between each point in the data set and each centroid, and divides the set to which that centroid belongs.

StepA2-5, dividing all data into sets, having K sets in total, and then recalculating the centroid of each set.

StepA2-6, if the distance between the newly calculated centroid and the original centroid is less than the convergence requirement of the algorithm, the clustering can be considered to have reached the desired result and the algorithm terminates.

StepA2-7, if the new centroid and the original centroid distance do not meet the convergence requirement, the steps d-f need to be iterated.

And step B2, re-analyzing each group category aiming at the clustering group categories, and re-screening the abnormality according to the distribution condition of the abnormal score of each group. The method comprises the following steps:

StepB2-1, study each group characteristics, and incorporate the groups exceeding the early warning value of dissolved gas in oil into the abnormality.

StepB2-2, screening the groups which do not exceed the early warning value into an attention value and bringing the attention value into a final abnormal result by setting an abnormal score threshold value for the groups which do not exceed the early warning value and approach the early warning value through abnormal score and gas content probability distribution.

StepB2-3, aggregate the abnormality clusters and the "attention values" to obtain the abnormality detection result.

It should be noted that the setting of the manual threshold needs to be performed in combination with actual data conditions, and is not limited to a fixed value, as long as abnormal data of this type is found through the threshold. In addition, the calculation speed characteristic can be flexibly set according to specific research time, and is not limited to a fixed time range.

Example 3:

based on the same inventive concept, the invention also provides a system for detecting the abnormality of the dissolved gas in the transformer oil, as shown in fig. 7.

The system comprises: the system comprises a rate characteristic extraction module, an abnormity initial detection module and an abnormity final detection module;

the system comprises a rate characteristic extraction module, a data acquisition module and a data analysis module, wherein the rate characteristic extraction module is used for acquiring the gas content in a monitoring data set of each gas dissolved in the transformer oil and extracting rate characteristics according to the gas content to obtain the gas production rate of each gas;

the abnormal initial detection module is used for respectively carrying out abnormal initial detection on the gas production rate and the gas content of each gas through an isolated forest algorithm to obtain abnormal scores of each gas;

and the abnormal final inspection module is used for clustering the abnormal scores of the gases and performing abnormal final inspection on the gases according to the clustering result, the abnormal scores of the gases, the gas production rate of the gases and the content of the gases to obtain the abnormal final inspection result of the gases dissolved in the transformer oil.

The abnormity final inspection module comprises: a clustering unit and a final inspection unit;

the clustering unit is used for clustering the abnormal scores of the gases by a K-Means clustering method to obtain clustering results of the abnormal scores of the gases;

and the final inspection unit is used for performing abnormal final inspection on the gas on the abnormal scores of the gases, the gas production rate of the gases and the content of the gases based on the set content early warning value and the set gas production rate early warning value of the gases in the oil according to the distribution condition of the abnormal scores of the gases of each category in the clustering result, so as to obtain the abnormal final inspection result of the gases dissolved in the transformer oil.

The anomaly score for a sample in the subset is calculated as:

in the formula (I), the compound is shown in the specification,is the anomaly score for sample X, X being the sample in sample subset X, E [ h (X)]Representing the expectation of the path length of sample x over a plurality of isolated trees of gas characteristics,representing the number of samples in each gas characteristic isolation tree of the sample subset X,for indicatingThe average path length of the gas characteristic isolated tree constructed by the strip samples;

wherein is made ofAverage path length of isolated tree of gas features constructed by strip samplesCalculated as follows:

in the formula (I), the compound is shown in the specification,to sum the sums.

The gas production rate is calculated according to the following formula:

in the formula, V is the gas production rate, delta y is the variation of the gas content, delta t is the time interval, m is the total oil quantity of the equipment, and rho is the oil density;

wherein, the time interval Δ t is calculated according to the following formula:

in the formula, t_iIn order to monitor the time of day,at the moment of minimum gas content, y_iIs t_iGas content value, y, monitored at any time_i-daysIs at t_iMinimum gas content value in days before the moment, day is a variable time parameter.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the scope of protection thereof, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: after reading this disclosure, those skilled in the art will be able to make various changes, modifications and equivalents to the embodiments of the invention, which fall within the scope of the appended claims.