CN111738573A - Health evaluation method based on electric energy meter full life cycle data - Google Patents
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Abstract
The invention relates to a health evaluation method based on electric energy meter full life cycle data. The method is based on big data concept, deeply ploughs in the power industry, fuses internal data and external environment data of a power grid, conducts influence analysis of relevant factors of electric energy meter health, constructs a fault prediction model of the electric energy meter, reports dimensional data such as event times, acquisition success rate, electric quantity abnormal rate, operation duration, first inspection error and the like according to the assembly and disassembly times of the electric energy meter, evaluates the operation state of the electric energy meter, establishes a health evaluation system of the electric energy meter, and gives the health degree of the operation state of the electric energy meter.
Description
Technical Field
The invention belongs to the technical field of electric power big data application, and particularly relates to a health evaluation method based on electric energy meter full life cycle data.
Background
With the rapid development of the intelligent power grid technology, technologies such as an electronic transformer, an intelligent high-voltage electric appliance, an optical fiber Ethernet, an IEC61850 communication standard and the like are gradually popularized and applied in an intelligent substation. In order to implement the development strategy and service system of intelligent power grid and intelligent power utilization for the construction and construction of national power grid companies, the electric energy meter is popularized and applied along with the rise of the intelligent power grid in China. However, the problem that the electric energy surface installed in the early stage is subjected to the dismantling verification and reuse is solved, and the Hubei company establishes a dismantling table centralized detection mode first, so that the verification quality and the verification data are guaranteed to the greatest extent, and are real and reliable. However, the method is not perfect in the aspects of electric energy meter fault prediction and operation quality deep analysis and evaluation, the value of mass data of each existing system is not effectively utilized, and the research on electric energy meter fault trend analysis needs to be deeply developed, so that scientific guidance is provided for electric energy meter health evaluation and accurate replacement.
Disclosure of Invention
The invention aims to fully develop the method for analyzing the fault influence factors and evaluating the health of the electric energy meter by utilizing the verification data and the daily metering data of the current electric energy meter. The method is characterized by comprising the steps of ploughing deeply in the power industry based on a big data concept, fusing internal data and external environment data of a power grid, conducting influence analysis on health-related factors of the electric energy meter, constructing a fault prediction model of the electric energy meter, reporting event times, collecting success rate, collecting dimension data such as electric quantity abnormal rate, operation duration and first inspection error according to the assembling and disassembling times of the electric energy meter, evaluating the operation state of the electric energy meter, establishing a health evaluation system of the electric energy meter, and giving the health degree of the operation state of the electric energy meter.
The technical scheme of the invention is as follows:
a health evaluation method based on the whole life cycle data of an electric energy meter integrates data requirements by taking the electric energy meter as a node according to the whole life cycle data of the electric energy meter, completes data cleaning and feature screening, extracts fault factors and feature attributes of the electric energy meter through a data mining algorithm, performs health scoring on the electric energy meter by using a comprehensive evaluation algorithm, and divides health grades.
Furthermore, the integration of the data of the whole life cycle of the electric energy meter integrates all related data of the whole life cycle of the electric energy meter by associating the electric energy meter number with the cargo batch, the manufacturer, the carrier module, the region, the running time, the running environment and the history and removing the fault information data of the meter.
Further, the data cleaning is to eliminate abnormal data in the data by understanding the electric energy meter data and utilizing the Lauda criterion and the quartile, and to perform standard normal distribution or 0-1 standardization on the data, so that the data have the same dimension.
Furthermore, the characteristic screening is carried out by understanding the service of the electric energy meter and utilizing the variance contribution degree, so that the data characteristics are simplified, and the running speed of the model is increased.
Further, the XGboost classification algorithm is applied to extracting fault factors and characteristics which affect the service life of the electric energy meter by the data mining algorithm. Constructing an XGboost algorithm model according to the life cycle data of the electric energy meter, wherein the XGboost algorithm model is used for fault factor classification and extracting the characteristics which have main influence on the electric energy meter;
the comprehensive evaluation algorithm is based on the defects of the TOPSIS comprehensive evaluation method, the data mining algorithm is used for extracting the actual property, the objectivity and the practicability of index data, and the comprehensive evaluation method is modified, so that the whole health evaluation system is more reasonable and more stable.
Further, the health scoring is used for dividing health grades, and the electric energy meter is divided into different health grades according to the comprehensive score of the electric energy meter: the normal, early warning, abnormity and repair reporting functions can provide reference for the replacement, verification and purchase of the electric energy meter of the electric power enterprise, and promote the improvement of the operation benefit of the electric power enterprise.
Furthermore, the integrated data extracts fault data of the electric energy meter at different stages from a metering production scheduling platform, a power utilization information acquisition system and a marketing business application system in a big data environment, so that data redundancy is removed, and data quality and data flexibility are improved.
The fault factors are subjected to XGboost algorithm model application, the main degree of the fault factors influenced by the service life of the electric energy meter is graded, and the fault factors are used for reference of electric energy meter maintainers and electric energy meter manufacturers.
Furthermore, the method for evaluating the health of the data of the whole life cycle of the electric energy meter fully utilizes the value of mass data of each system, deeply develops the analysis and research on the health trend of the electric energy meter by taking the electric energy meter as a core, and provides a new direction for the verification of the electric energy meter.
Furthermore, the data quality and the data flexibility are improved, a traceable evaluation system of a large data platform system is realized in terms of data integrity, data accuracy, data consistency and data completeness, and data support and service support are provided for electric energy meter service evaluation.
Furthermore, the manufacturer of the electric energy meter refers to the reference to guide the manufacturer to perform model selection and improvement on the electric energy meter elements, reduce the risk of familial faults, develop targeted measures for the fault types, and realize accurate replacement and operation and maintenance.
The invention has the advantages that:
1. the invention does not need additional equipment investment.
2. The invention effectively reduces the complaint risk of the user, improves the satisfaction degree of the user and improves the image of the national network company
3. The invention can find the faults of the electric energy meter in time, reduce the risk hidden danger of the faults, can carry out differential accurate replacement and reduce equipment investment and waste.
4. The invention can guide the screening work of the electric energy meter components and improve the metering quality of the electric energy meter.
5. The whole analysis process of the invention does not need manual intervention, saves a large amount of human resources and simultaneously improves the accuracy of the analysis result.
6. The algorithm of the invention fully combines the mathematical algorithm and the daily life rule, and has the advantages of simple and practical algorithm, high accuracy, good practicability and strong popularization.
7. Guiding the electric energy meter to be replaced by hardcover: through the electric energy meter fault prediction, the electric energy meter fault can be timely found, the field operation and maintenance is guided, the fault risk hidden danger is reduced, the electric energy meter replacement and the selective inspection scheme strategy are automatically generated, the differentiated accurate replacement is carried out, and the equipment investment and waste are reduced.
Drawings
FIG. 1 is a model diagram of health evaluation according to the present invention.
FIG. 2 is an exemplary diagram of a CART tree according to the present invention.
FIG. 3 is an exemplary diagram of a stack of CART trees of the present invention.
FIG. 4 is a flow chart of model construction according to the present invention.
FIG. 5 is a diagram of the method concept of the present invention.
Detailed Description
The invention provides a health evaluation method based on electric energy meter full-life cycle data, which needs to comprehensively consider information of arrival batches, manufacturers, carrier modules, regions, operation duration, operation environments (landforms, weather and the like), historical meter-detaching and meter-returning faults and the like of electric energy meters, combines index data of metering online monitoring events, station area acquisition success rate, station area line loss and the like, adopts an XGboost model to construct an electric energy meter fault prediction model to extract main characteristics and main fault factors, and then utilizes a TOPSIS evaluation model to score and evaluate the health degree of the electric energy meters.
1. Electric energy meter life cycle data
And extracting fault data (verification, operation, disassembly and the like) of the electric energy meter at different stages of the whole life from a metering production scheduling platform, an electricity utilization information acquisition system and a marketing business application system.
2. Health evaluation model of electric energy meter life cycle data (see figure 1)
The whole process of the invention can be divided into the following 3 stages:
(1) and extracting data of the meter during the running period, disassembling the verification data, the weather data and the like, integrating the data according to the business rule, dividing the data into qualified and unqualified conclusions according to the verification condition, and performing data preprocessing work.
(2) And classifying the electric energy meter by adopting an XGboost algorithm, and extracting main characteristics and main fault factors.
(3) And the TOPSIS scoring model is used for scoring and evaluating the electric energy meter.
Introduction of XGboost Algorithm
The XGboost (extreme Gradient boosting) is named after the whole name, is the king card of an integrated learning method, is very top in the performance of the XGboost on most regression and classification problems, and is introduced in more detail below.
XGboost is actually a stack of CART trees. The result of predicting a stack of CART trees is to add the predicted values of each tree together as the final predicted value.
The following figures are examples of CART tree fig. 2 and a stack of CART trees to determine whether a person would like a computer game:
the bottom of fig. 3 illustrates how a stack of CART trees can be used for prediction, simply by summing the prediction scores of the individual trees. Briefly, for the classification problem, since the corresponding value of the leaf node of the CART tree is an actual score, not a definite category, it is beneficial to implement an efficient optimization algorithm.
This model is mathematically represented accurately as follows:
here, K is the number of trees, F represents all possible CART trees, and F represents a specific CART tree. This model consists of K CART trees.
The objective function of the model is as follows:
the objective function also comprises two parts, the first part is a loss function, and the second part is a regularization term, wherein the regularization term is obtained by adding regularization terms of K trees.
Introduction to TOPSIS Algorithm
The TOPSIS method is also called an ideal solution method and is an effective multi-index evaluation method.
Firstly, setting a multi-attribute decision scheme set as D { D1, D2, D3, … dm, and setting a variable for measuring the quality of scheme attributes as x1,…xnThe vector formed by n attribute values of each scheme in the scheme set D is ai1,…ainAs a point in the n-dimensional space, it uniquely represents a certain scheme.
And then constructing a positive and negative ideal solution, C*C0The attribute value is the optimal/inferior value of the attribute in the decision matrix. In n-dimensional space, the solutions are collected into DScheme and C*C0The distance of the optimal solution is compared, and the scheme which is close to the positive ideal solution and is far away from the negative ideal solution is the optimal scheme.
The calculation steps are described as follows:
(1) let the decision matrix be A ═ aij)m×n(in the decision-making process, the decision-making and evaluation results are affected due to different decision attribute types, different attribute dimensions and different attribute values), the attribute values are normalized (the method is not unique and can be determined according to specific situations), and a normalized decision matrix B is set as (B)ij)m×nWherein
(2) Constructing a weighted canonical matrix C ═ (C)ij)m×n. Let the weight vector w given by the decision maker be w ═ w1,w2,…,wn]TThen, then
cij＝wij·bij,i＝1,2,....m；j＝1,2,…,n.
(3) Determining a positive ideal solution C*Sum negative ideal solution C0
Ideal solution
Negative ideal solution
(4) Calculating the distance of each scheme to the positive (negative) ideal solution
In the same way
(5) Calculating a comprehensive evaluation value
5. Electric energy meter health evaluation model
Firstly, the XGboost algorithm is used for predicting the state information of all electric energy meters, then a TOPSIS algorithm is combined with the life cycle data of the electric energy meters to construct an electric energy meter health evaluation model, and the specific model establishing process is shown in figure 4:
(1) and (4) preprocessing the data of the whole life cycle of the electric energy meter. The data includes: the method comprises the following steps of (1) archive data and identification data in an MDS system, wherein the archive data comprise electric energy meter numbers, arrival batches, manufacturers, metering chips, batteries and the like, and the identification data comprise verification errors, fault types, clock unit detection, battery unit detection, communication unit detection and the like; collecting data of a system, wherein the data comprises operation events and an electrical environment, the operation events comprise overcurrent, voltage loss, phase failure, tripping, power failure and the like, and the electrical environment comprises daily frozen electric quantity readings, 24-point voltage data of a distribution area and the like; and an external environment including an electric energy meter installation location: indoor/outdoor, maximum temperature, minimum temperature, humidity, etc. Through data understanding, filling processing is carried out on null values in data, abnormal data in the data are removed by utilizing a Layouda criterion and a quartile, so that the accuracy of the data entering a model is ensured, and standard normal distribution or 0-1 standardization is carried out on the data, so that the data have the same dimension and the model is favorable for fast convergence.
(2) And determining the state information of the electric energy meter. And training an XGboost model according to data obtained by preprocessing the full life cycle data of the electric energy meter, and extracting main characteristics about the life of the electric energy meter and main fault reasons of the electric energy meter by using the model.
(3) And establishing an electric energy meter evaluation model. Extracting main characteristics influencing the service life of the electric energy meter according to the XGboost model, eliminating unnecessary characteristics, and then establishing an electric energy meter health evaluation model by utilizing a TOPSIS algorithm.
6. The invention provides a method for establishing health evaluation of life cycle data of an electric energy meter, wherein XGboost (extreme gradient boost) is adopted to extract main factors and fault factors influencing the life of the electric energy meter due to more data quantity of the life cycle of the electric energy meter, and a TOPSIS evaluation algorithm is introduced to establish a health evaluation model of the electric energy meter; the method not only considers the influence factors of the electric energy meter in the life cycle, increases the stability of the system, but also considers the practicality and objectivity of index data, corrects the defects of the TOPSIS comprehensive evaluation method, and enables the whole evaluation system to be more reasonable. In addition, the electric energy meter business is divided into different health levels according to the comprehensive score of the electric energy meter, a certain reference function can be provided for electric power enterprises to purchase and replace the electric energy meter, and the improvement of the operation benefit of the electric power enterprises is promoted.
Firstly, training a fault prediction model by using various data in the operation process of xx ten thousand online electric energy meters through XGboost, then testing the on-line electric energy meters, predicting the number of the on-line electric energy meters with faults and non-faults, obtaining main factors influencing the service life of the electric energy meters, grading the electric energy meters by using a TOPSIS health evaluation model, and grading the health degree of the electric energy meters according to the grading result: "normal, early warning, abnormal, repair" (degree words can be changed according to the situation). The 'repair reporting' electric energy meter is the worst health degree, the metering result of the electric energy meter cannot be normally used, and the electric energy meter needs to be replaced immediately; the abnormal electric energy meter needs to follow up the health degree in real time, and the measurement result can be used as a reference and needs to be replaced; the early warning electric energy meter can be normally used, the metering result is normal, and the health state of the electric energy meter still needs to be concerned; a "normal" meter is a meter that has a higher health score, but still requires attention. The specific method idea is shown in fig. 5:
obtaining the service life related factors of the electric energy meter through an XGboost algorithm:
Claims (10)
1. a health evaluation method based on electric energy meter full life cycle data is characterized in that: according to the whole life cycle data of the electric energy meter, the electric energy meter is taken as a node, data requirements are integrated, data cleaning and feature screening are completed, fault factors and feature attributes of the electric energy meter are extracted through a data mining algorithm, the electric energy meter is subjected to health scoring through a comprehensive evaluation algorithm, and health grades are divided.
2. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: and integrating the data of the whole life cycle of the electric energy meter by correlating the number of the electric energy meter with the cargo batch, the manufacturer, the carrier module, the region, the running time, the running environment and the history and removing the fault information data of the meter, and integrating all related data of the whole life cycle of the electric energy meter.
3. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: the data cleaning is to eliminate abnormal data in the data by understanding the electric energy meter data and utilizing the Lauda criterion and the quartile, and to perform standard normal distribution or 0-1 standardization on the data, so that the data have the same dimension.
4. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: the characteristic screening is carried out by understanding the service of the electric energy meter and utilizing the variance contribution degree, so that the data characteristic is simplified, and the running speed of the model is increased.
5. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: the XGboost classification algorithm is applied to extracting fault factors and characteristics which affect the service life of the electric energy meter;
constructing an XGboost algorithm model according to the life cycle data of the electric energy meter, wherein the XGboost algorithm model is used for fault factor classification and extracting features influencing the electric energy meter;
the comprehensive evaluation algorithm is based on the defects of the TOPSIS comprehensive evaluation method, the data mining algorithm is used for extracting the actual property, the objectivity and the practicability of index data, and the comprehensive evaluation method is modified, so that the whole health evaluation system is more reasonable and more stable.
6. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: the health scoring is to divide the health grade, and the electric energy meter is divided into different health grades according to the comprehensive score of the electric energy meter: "normal, early warning, abnormal, repair".
7. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 1, characterized in that: the integrated data extracts fault data of the electric energy meter at different stages from a metering production scheduling platform, a power utilization information acquisition system and a marketing business application system in a big data environment, and removes data redundancy;
the fault factors are subjected to XGboost algorithm model application, and the fault factors influenced by the service life of the electric energy meter are classified into main degrees according to grades.
8. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 6, characterized in that: the health evaluation method for promoting the operation benefits of the power enterprises fully utilizes the mass data value of each system, deeply develops the health trend analysis research of the electric energy meter by taking the electric energy meter as a core, and provides a new direction for meter verification.
9. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 7, characterized in that: the data quality and the data flexibility are improved, and a traceable evaluation system of a large data platform system is realized in terms of data integrity, data accuracy, data consistency and data completeness.
10. The health evaluation method based on the data of the full life cycle of the electric energy meter according to claim 9, characterized in that: the electric energy meter manufacturer refers to the electric energy meter manufacturer, guides the manufacturer to carry out electric energy meter component type selection and improvement, reduces familial fault risks, and carries out targeted measures on fault types.
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