CN113284004A - Power data diagnosis treatment method based on isolated forest algorithm - Google Patents
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Abstract
The invention relates to a power data diagnosis treatment method based on an isolated forest algorithm, which comprises the following steps: collecting data: collecting the operation data of the wind generating set by using an SCADA (supervisory control and data acquisition); data preprocessing: preprocessing the acquired data to form a standard data set; and (3) isolated forest algorithm analysis: analyzing the data set by using an isolated forest algorithm, isolating abnormal data and reporting the abnormal data to a control system; and (4) judging expert knowledge, namely intelligently analyzing the reported abnormal data by using a judging system fused with expert experience information so as to judge whether the running state is abnormal or not, and sending a judgment result to an operator on duty. The beneficial effects are that: the automatic and timely treatment method is realized, and the analysis by adopting the isolated forest algorithm has the advantages of high processing speed and high processing accuracy.
Description
Technical Field
The invention relates to the technical field of power data processing, in particular to a power data diagnosis treatment method based on an isolated forest algorithm.
Background
An scada (supervisory Control And Data acquisition) system, i.e. a Data acquisition And monitoring Control system. The SCADA system is a DCS and electric power automatic monitoring system based on a computer; the method has wide application field, and can be applied to a plurality of fields such as data acquisition and monitoring control, process control and the like in the fields of electric power, metallurgy, petroleum, chemical industry, gas, railways and the like.
The SCADA measurement information of the wind generating set is not matched with the normal operation condition of the wind generating set. The unit is in abnormal operation states such as starting and stopping, but the duration is very short and is not correctly identified by the controller; the problems of range deviation and multiplication of scaling proportion occur in the measurement process of the sensor, measurement data are abnormal, and due to the fact that the data volume is large, the requirement for automatic and timely treatment cannot be met depending on manpower, and therefore an automatic and timely treatment method needs to be researched.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a power data diagnosis governing method based on an isolated forest algorithm.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a power data diagnosis treatment method based on an isolated forest algorithm comprises the following steps:
collecting data: collecting the operation data of the wind generating set by using an SCADA (supervisory control and data acquisition);
data preprocessing: preprocessing the acquired data to form a standard data set;
and (3) isolated forest algorithm analysis: analyzing the data set by using an isolated forest algorithm, isolating abnormal data and reporting the abnormal data to a control system;
and (4) judging expert knowledge, namely intelligently analyzing the reported abnormal data by using a judging system fused with expert experience information so as to judge whether the running state is abnormal or not, and sending a judgment result to an operator on duty.
Wherein the data preprocessing comprises: cleaning the key characteristic field; the empty value is manually supplemented, and the empty value which cannot be supplemented is removed; carrying out manual modification on the abnormal value, and removing the abnormal value which cannot be modified; and calculating and forming the features to be refined according to a feature calculation formula, and standardizing the features to form a standard data set.
The isolated forest algorithm analysis comprises isolated tree training, integration of all isolated tree results and isolation of abnormal data and reporting of the abnormal data to a control system.
Wherein the training of the isolated tree specifically comprises:
s11, randomly selecting psi data from the data set as subsamples and putting the subsamples into a root node of an isolated tree;
s12, randomly appointing a dimension, randomly generating a cutting point p in the data range of the current node, and generating the cutting point p between the maximum value and the minimum value of the appointed dimension in the data of the current node;
s13, selecting the cutting point p to generate a hyperplane, and dividing the data space of the current node into 2 subspaces: placing points smaller than p in the currently selected dimension on the left branch of the current node, and placing points larger than or equal to p on the right branch of the current node;
s14, recursion steps S12 and S13 at the left and right branch nodes of the node, new leaf nodes are continuously constructed until only one piece of data on the leaf nodes can not be cut any more or the isolated tree has grown to the set height.
Wherein, the integrating all the isolated tree results specifically comprises:
s21, since the cutting process is completely random, the ensembles method is needed to converge the result, i.e. cut from the beginning repeatedly, and then calculate the average value of each cut result.
S22, after t isolated trees are obtained, training of a single isolated tree is finished, and then the generated isolated tree can be used to evaluate test data, that is, the anomaly score S is calculated, for each sample x, the result of each tree needs to be calculated comprehensively, and the anomaly score is calculated by the following formula:
h (x) is the height of x in each tree, c (Ψ) is the average of the path lengths at a given number of samples Ψ, and is used to normalize the path length h (x) of sample x;
s23, judging abnormal data:
if the anomaly score is close to 1, then it must be anomalous data;
if the anomaly score is much less than 0.5, then it must not be anomalous data;
if the scores of all points for an anomaly are around 0.5, then there is likely no anomalous data in the sample.
Wherein the expert knowledge discrimination specifically comprises:
s31, collecting historical operation abnormal data, calculating according to a characteristic calculation formula to form characteristics to be extracted, carrying out standardization processing on the characteristics, and then combining the historical operation abnormal data, the data characteristics after the standardization processing and abnormal data judgment results to construct an expert knowledge base;
s32, searching the standardized processing characteristics of the abnormal data analyzed by the isolated forest algorithm in an expert knowledge base, and collecting all search results;
s33, comparing the abnormal data analyzed by the isolated forest algorithm with the search results in the step S32 one by one, and finding out the same or similar search results;
and S34, sending the searched search result as a judgment result to the operator on duty.
The invention has the beneficial effects that: the method comprises the steps that operation data of the wind generating set are automatically acquired through an SCADA (supervisory control and data acquisition), a standard data set is formed after data processing, then, an isolated forest algorithm is used for analysis, abnormal data are isolated and reported to a control system, and finally, a judgment system fusing expert experience information is used for intelligently analyzing the reported abnormal data, so that whether the operation state is abnormal or not is judged, and a judgment result is sent to an operator on duty; therefore, the automatic and timely treatment method is realized, and the analysis by adopting the isolated forest algorithm has the advantages of high processing speed and high processing accuracy.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flow chart in an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 1, a governing method of power data diagnosis based on isolated forest algorithm includes the following steps:
collecting data: and collecting the operation data of the wind generating set by using the SCADA.
Data preprocessing, preprocessing the acquired data to form a standard data set, and specifically comprises the following steps: cleaning the key characteristic field; the empty value is manually supplemented, and the empty value which cannot be supplemented is removed; carrying out manual modification on the abnormal value, and removing the abnormal value which cannot be modified; and calculating and forming the features to be refined according to a feature calculation formula, and standardizing the features to form a standard data set. The data sets are separated according to time periods, for example, the operation data of the wind generating set collected in the time period of 0-1 point of a certain day of a certain month in a certain year are preprocessed to form a standard data set, and the length and the end point of the time period can be set manually.
And (4) analyzing the isolated forest algorithm, namely analyzing the data set by using the isolated forest algorithm, isolating abnormal data and reporting the abnormal data to the control system, wherein the isolated forest algorithm has the advantages of high processing speed and high processing accuracy. The isolated forest algorithm analysis comprises isolated tree training, integration of all isolated tree results and isolation of abnormal data and reporting of the abnormal data to a control system.
The training of the isolated tree specifically comprises the following steps:
s11, randomly selecting psi data from the data set as subsamples and putting the subsamples into a root node of an isolated tree;
s12, randomly appointing a dimension, randomly generating a cutting point p in the data range of the current node, and generating the cutting point p between the maximum value and the minimum value of the appointed dimension in the data of the current node;
s13, selecting the cutting point p to generate a hyperplane, and dividing the data space of the current node into 2 subspaces: placing points smaller than p in the currently selected dimension on the left branch of the current node, and placing points larger than or equal to p on the right branch of the current node;
s14, recursion steps S12 and S13 at the left and right branch nodes of the node, new leaf nodes are continuously constructed until only one piece of data on the leaf nodes can not be cut any more or the isolated tree has grown to the set height.
The step of integrating all the isolated tree results specifically comprises the following steps:
s21, since the cutting process is completely random, the ensembles method is needed to converge the result, i.e. cut from the beginning repeatedly, and then calculate the average value of each cut result.
S22, after t isolated trees are obtained, training of a single isolated tree is finished, and then the generated isolated tree can be used to evaluate test data, that is, the anomaly score S is calculated, for each sample x, the result of each tree needs to be calculated comprehensively, and the anomaly score is calculated by the following formula:
h (x) is the height of x in each tree, c (Ψ) is the average of the path lengths at a given number of samples Ψ, and is used to normalize the path length h (x) of sample x;
s23, judging abnormal data: if the anomaly score is close to 1, then it must be anomalous data; if the anomaly score is much less than 0.5, then it must not be anomalous data; if the scores of all points for an anomaly are around 0.5, then there is likely no anomalous data in the sample.
And (3) judging expert knowledge, namely intelligently analyzing the reported abnormal data by using a judging system fused with expert experience information so as to judge whether the running state is abnormal or not, and sending a judgment result to an operator on duty to realize an automatic and timely treatment method, wherein the judgment of the expert knowledge specifically comprises the following steps:
s31, collecting historical operation abnormal data, calculating according to a characteristic calculation formula to form characteristics to be extracted, carrying out standardization processing on the characteristics, and then combining the historical operation abnormal data, the data characteristics after the standardization processing and abnormal data judgment results to construct an expert knowledge base;
s32, searching the standardized processing characteristics of the abnormal data analyzed by the isolated forest algorithm in an expert knowledge base, and collecting all search results;
s33, comparing the abnormal data analyzed by the isolated forest algorithm with the search results in the step S32 one by one, and finding out the same or similar search results;
and S34, sending the searched search result as a judgment result to the operator on duty.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (6)
1. A power data diagnosis governing method based on an isolated forest algorithm is characterized by comprising the following steps:
collecting data: collecting the operation data of the wind generating set by using an SCADA (supervisory control and data acquisition);
data preprocessing: preprocessing the acquired data to form a standard data set;
and (3) isolated forest algorithm analysis: analyzing the data set by using an isolated forest algorithm, isolating abnormal data and reporting the abnormal data to a control system;
and (4) judging expert knowledge, namely intelligently analyzing the reported abnormal data by using a judging system fused with expert experience information so as to judge whether the running state is abnormal or not, and sending a judgment result to an operator on duty.
2. The abatement method of claim 1, wherein the data pre-processing comprises: cleaning the key characteristic field; the empty value is manually supplemented, and the empty value which cannot be supplemented is removed; carrying out manual modification on the abnormal value, and removing the abnormal value which cannot be modified; and calculating and forming the features to be refined according to a feature calculation formula, and standardizing the features to form a standard data set.
3. A remediation method according to claim 1 wherein: the isolated forest algorithm analysis comprises isolated tree training, integration of all isolated tree results and isolation of abnormal data and reporting of the abnormal data to a control system.
4. The governance method according to claim 3, wherein the orphan tree training specifically comprises:
s11, randomly selecting psi data from the data set as subsamples and putting the subsamples into a root node of an isolated tree;
s12, randomly appointing a dimension, randomly generating a cutting point p in the data range of the current node, and generating the cutting point p between the maximum value and the minimum value of the appointed dimension in the data of the current node;
s13, selecting the cutting point p to generate a hyperplane, and dividing the data space of the current node into 2 subspaces: placing points smaller than p in the currently selected dimension on the left branch of the current node, and placing points larger than or equal to p on the right branch of the current node;
s14, recursion steps S12 and S13 at the left and right branch nodes of the node, new leaf nodes are continuously constructed until only one piece of data on the leaf nodes can not be cut any more or the isolated tree has grown to the set height.
5. The governance method according to claim 4, wherein the integrating all orphan tree results specifically comprises:
s21, since the cutting process is completely random, the ensembles method is needed to converge the result, i.e. cut from the beginning repeatedly, and then calculate the average value of each cut result.
S22, after t isolated trees are obtained, training of a single isolated tree is finished, and then the generated isolated tree can be used to evaluate test data, that is, the anomaly score S is calculated, for each sample x, the result of each tree needs to be calculated comprehensively, and the anomaly score is calculated by the following formula:
h (x) is the height of x in each tree, c (Ψ) is the average of the path lengths at a given number of samples Ψ, and is used to normalize the path length h (x) of sample x;
s23, judging abnormal data:
if the anomaly score is close to 1, then it must be anomalous data;
if the anomaly score is much less than 0.5, then it must not be anomalous data;
if the scores of all points for an anomaly are around 0.5, then there is likely no anomalous data in the sample.
6. The abatement method of claim 1, wherein the expert knowledge discrimination specifically comprises:
s31, collecting historical operation abnormal data, calculating according to a characteristic calculation formula to form characteristics to be extracted, carrying out standardization processing on the characteristics, and then combining the historical operation abnormal data, the data characteristics after the standardization processing and abnormal data judgment results to construct an expert knowledge base;
s32, searching the standardized processing characteristics of the abnormal data analyzed by the isolated forest algorithm in an expert knowledge base, and collecting all search results;
s33, comparing the abnormal data analyzed by the isolated forest algorithm with the search results in the step S32 one by one, and finding out the same or similar search results;
and S34, sending the searched search result as a judgment result to the operator on duty.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115964216A (en) * | 2023-01-30 | 2023-04-14 | 北京慧图科技(集团)股份有限公司 | Internet of things equipment data anomaly detection method based on isolated forest |
| CN117294017A (en) * | 2023-10-07 | 2023-12-26 | 南方电网调峰调频(广东)储能科技有限公司 | Multi-parameter comprehensive analysis energy storage power station state monitoring method and system |
| CN117407822A (en) * | 2023-12-12 | 2024-01-16 | 江苏新希望生态科技有限公司 | Full-automatic bud seedling machine and control method thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108732494A (en) * | 2017-04-21 | 2018-11-02 | 上海电气集团股份有限公司 | A kind of wind-driven generator abnormity diagnosis processing system |
| CN110430260A (en) * | 2019-08-02 | 2019-11-08 | 哈工大机器人（合肥）国际创新研究院 | Robot cloud platform based on big data cloud computing support and working method |
| CN111798312A (en) * | 2019-08-02 | 2020-10-20 | 深圳索信达数据技术有限公司 | Financial transaction system abnormity identification method based on isolated forest algorithm |
| CN112362292A (en) * | 2020-10-30 | 2021-02-12 | 北京交通大学 | Method for anomaly detection of wind tunnel test data |
-
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- 2021-05-10 CN CN202110506063.4A patent/CN113284004A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108732494A (en) * | 2017-04-21 | 2018-11-02 | 上海电气集团股份有限公司 | A kind of wind-driven generator abnormity diagnosis processing system |
| CN110430260A (en) * | 2019-08-02 | 2019-11-08 | 哈工大机器人（合肥）国际创新研究院 | Robot cloud platform based on big data cloud computing support and working method |
| CN111798312A (en) * | 2019-08-02 | 2020-10-20 | 深圳索信达数据技术有限公司 | Financial transaction system abnormity identification method based on isolated forest algorithm |
| CN112362292A (en) * | 2020-10-30 | 2021-02-12 | 北京交通大学 | Method for anomaly detection of wind tunnel test data |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115964216A (en) * | 2023-01-30 | 2023-04-14 | 北京慧图科技(集团)股份有限公司 | Internet of things equipment data anomaly detection method based on isolated forest |
| CN117294017A (en) * | 2023-10-07 | 2023-12-26 | 南方电网调峰调频(广东)储能科技有限公司 | Multi-parameter comprehensive analysis energy storage power station state monitoring method and system |
| CN117407822A (en) * | 2023-12-12 | 2024-01-16 | 江苏新希望生态科技有限公司 | Full-automatic bud seedling machine and control method thereof |
| CN117407822B (en) * | 2023-12-12 | 2024-02-20 | 江苏新希望生态科技有限公司 | Full-automatic bud seedling machine and control method thereof |
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