CN110543500A - Power transmission and transformation equipment health assessment platform based on big data - Google Patents
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Abstract
The invention discloses a health assessment platform for power transmission and transformation equipment based on big data, which comprises a data monitoring module, wherein the monitoring module comprises an online monitoring data module, a spot monitoring data module and an environmental data monitoring platform, acquired data information is transmitted to a multi-data acquisition module of the power transmission and transformation equipment to continue centralized data preprocessing, the multi-data acquisition module of the power transmission and transformation equipment transmits the monitoring information data to a multi-data fusion module of the power transmission and transformation equipment, the multi-data fusion module of the power transmission and transformation equipment transmits the power transmission and transformation data to a health decision analysis module of intelligent power transmission and transformation equipment, and the health decision analysis module of the intelligent power transmission and transformation equipment is electrically connected with a data index library and two cloud resource libraries of an intelligent prescription library. On the basis of putting higher requirements on the existing equipment state monitoring parameters, the invention takes mass data as a mining object, and can effectively integrate and fuse mass data with multiple time scales and multiple space-time dimensions.
Description
Technical Field
The invention relates to the technical field of power equipment monitoring, in particular to a health assessment platform for power transmission and transformation equipment based on big data.
background
in an evaluation model of the traditional method, equipment state monitoring quantity is an input parameter, and equipment defects and faults are output parameters; in the big data mining analysis method, equipment state monitoring quantity, equipment defects and faults are input parameters, and output quantity is the association rule, association degree and other elements among all input parameters. Traditional models formed by input and output volume training, once generated, cannot be changed unless retrained; the big data mining analysis method model adopts a dynamic correlation coefficient matrix to model the correlation between the equipment state index and the equipment state monitoring parameter, can continuously perform regression correction, can flexibly change according to the researched equipment object, state parameter, fault type and the like, does not need to be built again, and does not have the problem of model solidification. The power transmission and transformation equipment state evaluation method based on big data mining is suitable for evaluating and predicting any parameter index of equipment, including equipment health, load capacity and the like. The invention utilizes big data technology to process mass data and can analyze and excavate useful and valuable information from complex data.
disclosure of Invention
The invention aims to provide a health evaluation platform of power transmission and transformation equipment based on big data, which aims to excavate useful and valuable information by using a big data analysis method.
In order to achieve the purpose, the invention adopts the following technical scheme:
The utility model provides a power transmission and transformation equipment health assessment platform based on big data, includes data monitoring module, monitoring module includes on-line monitoring data module, area monitoring data module and environmental data monitoring platform, on-line monitoring data module with area monitoring data module carries out on-line monitoring to the data in the power transmission and transformation equipment, and through state monitoring agent CMA module with data transmission to the provincial power networks data center, environmental data monitoring platform collects meteorological data, mountain fire data and icing data, and the data information transmission that firstly gathers through the multichannel is first to the data information collection module that gathers, and secondly continues concentrated data preliminary treatment to the power transmission and transformation equipment multivariate data acquisition module with the data transmission and transformation equipment multivariate data fusion module, the power transmission and transformation equipment multivariate data fusion module carries out data cleaning to the power transmission and transformation data that receive, and the power transmission and transformation equipment multivariate data fusion module, The intelligent power transmission and transformation equipment health decision analysis method comprises the following steps of data integration, data transformation and data specification, wherein the power transmission and transformation equipment multivariate data fusion module transmits power transmission and transformation data to an intelligent power transmission and transformation equipment health decision analysis module, the intelligent power transmission and transformation equipment health decision analysis module is electrically connected with two cloud resource libraries, namely a data index library and an intelligent prescription library, the intelligent power transmission and transformation equipment health decision analysis module performs bidirectional interaction with the data index library and the intelligent prescription library, and the intelligent power transmission and transformation equipment health decision analysis module performs fitting by adopting an Aprior algorithm process.
preferably, the intelligent power transmission and transformation equipment health decision analysis module comprises three processes of data distribution optimization, data preprocessing in the module and analysis and evaluation processes.
preferably, the data distribution optimization process is as follows: when the MapReduce association query is executed, a standard Hadoop data layout scheme is adopted, the connection operation needs to be completed in a reduction stage, all data are grouped and sequenced on a plurality of nodes in a mapping stage, and then the TaskTracker node of a reduction task performs data pulling in a remote access mode.
preferably, the data preprocessing process in the module is as follows: and collecting abnormal state data of the power transmission and transformation equipment, wherein the important data comprises case data of faults and defects. The case code is denoted by As (s ═ 1,2, …,34), and the state parameter code is denoted by Wu (u ═ 1,2, …, 27). And then, carrying out state parameter assignment according to the characterization of the fault and the defect.
preferably, the analytical evaluation process is: and performing hierarchical clustering on each group of fault cases in the original data, and classifying all faults into 5 classes through preliminary analysis. And respectively selecting u as 1,2, … and 27, and comparing clustering effects by calculating contour coefficients of different clustering numbers u.
Compared with the traditional evaluation model, the method has the following advantages:
On the basis of proposing higher requirements on the existing equipment state monitoring parameters, the invention takes mass data as a mining object, applies a data mining method suitable for the mass data to mine the correlation among all factors to be analyzed in an uncertain model, and in the data mining analysis method, the most important is the effective integration and fusion of the mass data with multiple sources, multiple time scales and multiple space-time dimensions, thereby discovering the internal (known or hidden) correlation among various equipment state monitoring quantities, equipment defects and faults, even the various correlation which is difficult to integrate together on physical and logical models.
drawings
fig. 1 is a schematic structural diagram of a health evaluation platform of electric transmission and transformation equipment based on big data according to the present invention;
FIG. 2 is a stage diagram of a big data based data processing platform according to the present invention;
FIG. 3 is a schematic flow chart of an algorithm of a data distribution optimization process according to the present invention.
Detailed Description
the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to the attached drawings, the health assessment platform comprises a data monitoring module, the monitoring module comprises an online monitoring data module, a spot monitoring data module and an environmental data monitoring platform, the online monitoring data module and the spot monitoring data module perform online monitoring on data in the power transmission and transformation equipment, the data are transmitted to a provincial power grid data center through a state monitoring agent CMA module, the environmental data monitoring platform collects meteorological data, forest fire data and icing data, namely large data information is collected through multiple channels firstly, the collected data information is transmitted to a multi-element data collection module of the power transmission and transformation equipment to continue centralized data preprocessing, the multi-element data collection module of the power transmission and transformation equipment transmits monitoring information data to a multi-element data fusion module of the power transmission and transformation equipment, and the multi-element data fusion module of the power transmission and transformation equipment performs data cleaning on the received power transmission and transformation data, The intelligent power transmission and transformation equipment health decision analysis module is electrically connected with a data index library and an intelligent prescription library, the intelligent power transmission and transformation equipment health decision analysis module performs bidirectional interaction with the data index library and the intelligent prescription library, and the intelligent power transmission and transformation equipment health decision analysis module adopts an Aprior algorithm flow to perform fitting:
Aprior's algorithm (a frequent item set algorithm that mines association rules) flow can be described as follows:
Let D be the library to be analyzed of association rules, which is the collection of transactions T, if there are n transactions, D ═ { T1, T2, …, Tj, …, Tn }, and for each transaction, it consists of "m items", T ═ { T1, T2, …, Ti, …, Tm }.
For item set X, the definition of support S is:
And for the association rule of X ═ Y, the support degree is as follows:
S(X＝>Y)＝N(X∪Y)/D；
In the formula: s is the support degree; is the number of sets;
For an association rule with X ═ Y, its confidence level C is:
C(X＝>Y)＝S(X＝>Y)/S(X)
The confidence level described in this formula reflects the probability that if the item set contains X, then Y is also contained. For users using association rules, the users can mine association rules with higher support degree S and confidence degree C by defining minimum support degree and confidence degree threshold values.
In order to diagnose a failure mode, it is necessary to consider a correlation between each state parameter and each failure mode, that is, a possibility that a failure mode occurs when an abnormality occurs in a certain state parameter. After the correlation coefficient between each state parameter and each fault mode of the equipment is obtained, the equipment fault mode diagnosis matrix R can be obtained, and when the correlation coefficient Rrt is calculated, the Pearson correlation coefficient is adopted for calculation. The correlation coefficient is calculated by a product-difference method based on the dispersion of 2 variables and the average value of each variable, and the 2 dispersions are multiplied by each other, and the product is used to reflect the degree of correlation between the 2 variables. The Pearson correlation coefficient ranges from-1 to 1, a value of 0 indicates that there is no significant linear relationship between the 2 variables, and-1 and 1 indicate that 2 are completely negative or positive. The correlation coefficients of Br and Vt used for device fault diagnosis are given by formula (6) as r (bv) ═ Cov (Br, V);
in the formula: cov (Br, V) is the covariance of B and V;
after the diagnosis matrix R is obtained by the method, the fault data to be diagnosed can be diagnosed by a formula F ═ RU, wherein U is a fault case data vector to be diagnosed and comprises state parameter quantization levels of various state parameters; and F is a fault mode diagnosis result vector, and the value of each element in the vector can represent the membership degree of the fault case under each fault mode. In the final diagnosis of the most likely failure mode, the failure mode with the highest degree of membership (i.e., the highest numerical value) may be selected as the final result.
The intelligent power transmission and transformation equipment health decision analysis module comprises the following steps:
1. data distribution optimization
data needs to be distributed to each node in the cluster as uniformly as possible to keep load balance; node faults in the Hadoop cluster are regarded as a normal state, and the problem of node failure needs to be considered when data distribution is optimized; in order to ensure the reliability of data and the query processing efficiency, a multi-copy redundancy scheme is required; under the Hadoop running environment, network transmission and disk operation are important factors influencing overall performance, and if the data communication traffic can be reduced, the data processing time can be effectively reduced. Taking a data association query commonly used in a state monitoring platform as an example, when the MapReduce association query is executed, a standard Hadoop data layout scheme (data correlation is not considered) is adopted, and a connection operation needs to be completed in a reduction (Reduce) stage. In a mapping (Map) phase, all data are grouped and sequenced on a plurality of nodes, and then the TaskTracker node of the reduction task pulls the data in a remote access mode. In this process, there may be a large amount of data that is not relevant to the last connection operation that is also replicated and transmitted in the network. If the data of the same equipment is stored on the same node as much as possible according to the equipment attribute of the data during the data uploading, the connection operation can be completed in the mapping stage, the data communication in the reduction stage is omitted, and the overall execution efficiency is improved.
2. in-module data preprocessing
And collecting abnormal state data of the power transmission and transformation equipment, wherein the important data comprises case data of faults and defects. The case code is denoted by As (s ═ 1,2, …,34), and the state parameter code is denoted by Wu (u ═ 1,2, …, 27). And then, carrying out state parameter assignment according to the characterization of the fault and the defect. Because the knowledge graph construction only carries out mining analysis on the state parameters or the abnormal cases of the equipment, only 1 state parameter needs to be known whether to be abnormal or not, and the state grade or the deterioration degree of the state parameters are not related, so that only binary quantization is carried out. Defects can be classified into healthy, good, warning, worsening, and severe five-intermediate health states, depending on severity. When a certain state parameter is abnormal, Wu values of "1, 2, and 3" represent that the state parameter has a warning, deterioration, and serious defect, respectively, and Wu values of "0, 4 … 27" represent that the state parameter is healthy and good.
3. analytical evaluation process
And performing hierarchical clustering on each group of fault cases in the original data, and classifying all faults into 5 classes through preliminary analysis. And respectively selecting u as 1,2, … and 27, and comparing clustering effects by calculating contour coefficients of different clustering numbers u. As can be seen from table 1, u is 6, i.e., the clustering result is most ideal when the failure modes are classified into 5 classes. And 5 common fault modes of the transformer bushing can be summarized and summarized by combining with expert experience. The failure cases were clustered using k-means, and the results are shown in Table 1.
TABLE 1 failure case clustering results
According to the steps, the healthy song grade of the equipment is frozen, and the equipment is divided into five healthy states of health, good, warning, deterioration and serious according to the health index.
The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean "one and only one" unless specifically so stated, but rather "one or more. The term "exemplary" is used herein to mean "serving as an example, instance, or illustration. Any aspect described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects. The term "some" means one or more unless specifically stated otherwise. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element should be construed as a means-plus-function unless the element is explicitly recited using the phrase "means for.
Claims (6)
1. A big data-based health assessment platform for power transmission and transformation equipment comprises a data monitoring module, and is characterized in that the monitoring module comprises an online monitoring data module, a spot monitoring data module and an environmental data monitoring platform;
The online monitoring data module and the on-line monitoring data module perform online monitoring on data in the power transmission and transformation equipment, and transmit the data to a provincial power network data center through the state monitoring agent CMA module, the environmental data monitoring platform collects meteorological data, forest fire data and icing data, namely, firstly, big data information is collected through a plurality of channels, secondly, the collected data information is transmitted to the power transmission and transformation equipment multi-data collection module to continue centralized data preprocessing, then, the power transmission and transformation equipment multi-data collection module transmits monitoring information data to the power transmission and transformation equipment multi-data fusion module, the power transmission and transformation equipment multi-data fusion module performs data cleaning, data integration, data transformation and data specification on the received power transmission and transformation data, and then, the power transmission and transformation equipment multi-data fusion module transmits the power transmission and transformation data to the intelligent power transmission and transformation equipment health decision analysis module, the intelligent power transmission and transformation equipment health decision analysis module is electrically connected with a data index library and an intelligent prescription library, the intelligent power transmission and transformation equipment health decision analysis module is in bidirectional interaction with the data index library and the intelligent prescription library, and the intelligent power transmission and transformation equipment health decision analysis module adopts an Aprior algorithm flow for fitting.
2. The big-data-based health assessment platform for electric transmission and transformation equipment according to claim 1, wherein Aprior's algorithm flow is as follows:
Setting a library to be analyzed of the association rule as D, which is a collection of transactions T, if there are n transactions, D ═ { T1, T2, …, Tj, …, Tn }, and for each transaction, D ═ m items, { T1, T2, …, Ti, …, Tm };
for item set X, the definition of support S is:
And for the association rule of X ═ Y, the support degree is as follows:
S(X＝>Y)＝N(X∪Y)/D；
For an association rule with X ═ Y, its confidence level C is:
C(X＝>Y)＝S(X＝>Y)/S(X)。
3. the big data-based electric transmission and transformation equipment health assessment platform according to claim 1, wherein the intelligent electric transmission and transformation equipment health decision analysis module analysis steps are data distribution optimization, intra-module data preprocessing and analysis assessment processes respectively.
4. the big-data-based health assessment platform for electric transmission and transformation equipment according to claim 3, wherein the data distribution optimization process is:
when the MapReduce association query is executed, a standard Hadoop data layout scheme is adopted, the connection operation needs to be completed in a reduction stage, all data are grouped and sequenced on a plurality of nodes in a mapping stage, and then the TaskTracker node of a reduction task performs data pulling in a remote access mode.
5. The big-data-based health assessment platform for electric transmission and transformation equipment according to claim 1, wherein the data preprocessing process in the module is as follows:
collecting abnormal state data of the power transmission and transformation equipment, wherein case data with important points including faults and defects are collected, case codes are represented by As (s is 1,2, … and 34), state parameter codes are represented by Wu (u is 1,2, … and 27), and then state parameter assignment is carried out according to the characteristics of the faults and the defects.
6. The big-data-based health assessment platform for electric transmission and transformation equipment according to claim 1, wherein the analysis and assessment process is as follows:
And performing hierarchical clustering on each group of fault cases in the original data, classifying all faults into 5 classes through preliminary analysis, respectively selecting u as 1,2, … and 27, and comparing clustering effects by calculating contour coefficients of different clustering numbers u.
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| CN111177134B (en) * | 2019-12-26 | 2021-04-02 | 上海科技发展有限公司 | Data quality analysis method, device, terminal and medium suitable for mass data |
| CN111460027A (en) * | 2020-03-31 | 2020-07-28 | 贵州电网有限责任公司 | Intelligent dynamic monitoring method and system suitable for energy Internet |
| CN111460027B (en) * | 2020-03-31 | 2023-12-05 | 贵州电网有限责任公司 | Intelligent dynamic monitoring method and system suitable for energy Internet |
| CN113642171A (en) * | 2021-08-10 | 2021-11-12 | 国网福建省电力有限公司 | Power transmission and transformation equipment health state evaluation system and method based on big data |
| CN113742315A (en) * | 2021-08-17 | 2021-12-03 | 广州工业智能研究院 | Manufacturing big data processing platform and method |
| CN113988375A (en) * | 2021-09-28 | 2022-01-28 | 通号城市轨道交通技术有限公司 | Railway turnout fault prediction method and device |
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Application publication date: 20191206 |