The operation analysis system relies on the existing business system of the power plant for advanced application development, supporting the efficient and accurate operation and maintenance of the system.
A. Overall structure
The overall framework design of the system is shown in Figure 1.
1) Data layer. Based on the power plant common data model standard, the whole plant monitoring system (SIS) 15, the power plant electrical monitoring management system (ECMs) 16 are used to access the equipment asset operation and maintenance management system and the load electricity information acquisition system. Through data integration and cleaning, according to the actual requirements of operation analysis, the fault data, operation data, working condition factors ,and other data are deeply integrated to realize the serial collection of the multi-service data systems. This layer is composed of a graphic library and attribute database, which is stored in access/oracle. It provides basic general data service and database access ability for the platform layer to call.
2) The platform layer. Through the common data/service interface, the platform layer uses web service 17 and extract transform load (ETL) 18 to access external system data from the data layer; This layer is composed of operation framework construction and development platform modeling, including open source components of the workflow, transaction processing, security system ,and other platforms. The application platform running framework is composed of an operation basic framework, interface display framework, icon display framework,and application integration framework. The development platform modeling tool consists of business modeling, report definition, permission configuration, etc.
3) Application layer. The bottom layer uses Java, C, C + + and other programming languages, and based on echards chart library19, carries out front-end data visualization design, which is divided into data management, correlation analysis, fault risk prediction, weak point identification, system configuration and report analysis modules, providing charts, report reports,and other displays to realize multi-level user interaction.
B. Functional architecture
The core functions of the operation analysis system are realized by six modules in the application layer. Compared with the traditional analysis system, the system extends the functions of data correlation analysis, fault risk prediction,and weak point identification, and generates reports and visualizes the analysis and identification results.The system functional architecture is shown in Figure 2.
1) Data management module mainly realizes data integration, data query,and data processing.
2) In the correlation analysis module, the advanced algorithm is used to carry out the correlation analysis of multi-source heterogeneous data to realize the selection of optimal fault features and the screening of weak point identification indicators.
3) According to the fault data, operation data,and working condition factors, the fault risk prediction module carry out the fault setting; analyzes the cause of power failure, the scope of power failure, and the level of power failure; calculates the fault levels of various faults in regions, stations and feeders; analyzes the fault level, fault location, transfer,and optimization of the results; It realizes the functions of fault prediction, emergency repair decision-making and so on.
4) The weak point identification module comprehensively considers the network topology and operation characteristics to realize the analysis, scanning,and risk early warning of the weak link of auxiliary power.
5) The system configuration module mainly completes the automatic risk analysis calculation setting and risk analysis evaluation parameter setting.
6) The report analysis module mainly completes the correlation analysis, fault risk prediction,and weak point identification results query, generation,and editing, and then gives the transfer, optimization and equipmeIII.
critical technology
A. Data integration technology
The flow chart of data integration between an auxiliary power operation analysis system and part of auxiliary power information system20is shown in Figure 3.
1)Integrated design with application data of electrical primary and secondary equipment. The data integration of auxiliary power operation analysis system and electrical primary and secondary equipment system mainly includes standard code, file information of metering point, meter, low-voltage equipment, distribution transformer, etc., as well as the relationship between auxiliary power equipment, etc. Analyze the transmission data volume, transmission frequency,and other elements required for data integration with electrical primary and secondary equipment, and adopt the technical route of basic data platform of electrical primary and secondary equipment. The basic data platform is replicated by Ogg (Oracle Golden Gate) 21. Fig. 2 flow chart of system function realization application data of electrical primary and secondary equipment, auxiliary power operation analysis system obtains data from marketing basic data platform of a data center. When users, metering points, meter attributes, metering boxes are added, changed,or deleted, the basic data platform passes through Ogg as required The auxiliary power operation analysis system uses Java database connectivity (JDBC) mode to call and update data in real-time.
2)Data integration design with load information acquisition system. The acquisition system writes the power data and the bottom indication of electric energy meter into the platform through the standard interface of massive data, deploys the calculation service module of auxiliary power operation analysis system, and extracts the bottom indication of electric energy meter by the standard interface of the massive data platform.
3)And SIS data integration design. SIS pushes the data information of power transmission and distribution to the data center, and the auxiliary power operation analysis system extracts the equipment account information and network topology information required by the analysis system through ETL.
4)Data integration design with the plant automation system. The operation analysis system integrates the auxiliary power equipment and topology information, and the dispatch and control center generates the CIM format file 22, and the data center is responsible for data analysis.
5)Data integration design with the meteorological information system. The meteorological information system writes the temperature, humidity, wind speed and other weather data through the interface of the massive data platform.
B. Correlation analysis technology
Considering that many factors affecting the failure of auxiliary power, there are many redundant and irrelevant fault features. Multi-source data integrated by data management module is adopted Combined data of machinery, working condition, equipment,and electrical system, equipment location data, distribution transformer capacity, real-time load data, monthly maximum load data and outage time, outage times, etc., determine the fault factor sample set and conduct fault feature screening. The traditional filtering feature selection algorithm relief (relevant features) assigns different weights to features according to the relevance of each feature and category and sets the weight threshold to select features. ReliefF algorithm extends the ability of relief algorithm to deal with multi-classification problems 23. The correlation analysis technology proposed in this paper, after the feature selection based on ReliefF algorithm is completed, the correlation analysis of auxiliary power fault characteristics is carried out, and the redundant operation of fault characteristics is realized by improving the grey correlation analysis method, and finally,the optimal fault feature set is output (hereinafter referred to as g-relief algorithm). The specific process of correlation analysis of fault risk characteristics is shown in Figure 4.
1)The main fault characteristic matrix of auxiliary power is constructed, and then any feature is selected as "reference sequence", and the remaining feature is taken as "comparison sequence".
2)The correlation coefficient ξ (k) between the comparison sequence and the reference sequence at k time is solved respectively.
3) The correlation degree - ξ (k) between fault features is solved and the correlation matrix is established.
4) The correlation threshold is set to output the optimal fault feature set.
C. Fault risk prediction technology
The typical data prediction algorithms in machine learning theory include neural network algorithm, support vector machine (SVM), decision tree algorithm and lifting algorithm. The AdaBoost algorithm mentioned in this paper is a typical lifting algorithm, which can predict the risk level by upgrading the "weak classification algorithm" algorithm for the same training set. The algorithm flow is shown in the red dotted box in Figure 5. The weak classifier is trained by decision tree algorithm. Through the deviation of the weak classifier, the weight of the sample is updated, and then the strong classifier is obtained through repeated iterations. Finally, the fault prediction effect is improved. In the classification of fault risk level, considering that the scale of different power supply areas is very different, three kinds of fault risk indicators are selected, including the frequency of weekly fault outage, the proportion of outage duration and the proportion of lack of power supply. The outage risk degree of auxiliary power failure is divided into three levels: general, moderate and severe. The optimal feature set for fault risk level prediction is screened out by correlation analysis technology in Section 2.2, and the input sample set required in the prediction stage is effectively determined on the basis of this set. Firstly, the auxiliary power fault sample set is determined and imported, and the sample data is de dimensioned after "normalization"; the fault sample data is split randomly according to the proportion of 7:3, 70% of the data is used for training model, and 30% of the data is used for prediction; back propagation (BP) neural network, SVM algorithm and ada-dt are selected in machine learning The algorithm is used to predict the auxiliary power fault respectively, and the algorithm with the best effect is determined to train the fault risk prediction model by comparing the prediction accuracy.
D. Weak point identification technology
Considering the safety, economy and network topology of auxiliary power. In this paper, the network elastic performance change risk, branch load rate change risk, node voltage deviation risk and branch line loss rate change risk are introduced as key node identification indicators. Combined with subjective experience and objective data, the comprehensive evaluation method is used to analyze the index weight, and a multi-level and multi angle comprehensive evaluation model of auxiliary power key nodes is constructed. The flow chart of identification method of auxiliary power book vulnerability is shown in Figure A1 .The main steps are as follows:
1)Rread the topology data and operation state data of the auxiliary power network, calculate the power flow of the initial stable state of the auxiliary power system, and conduct node n-1 Fault simulation test;
2) Calculate the index values of network elastic change value, node voltage deviation risk, branch load change risk and line loss change risk in the initial stable state of auxiliary power;
3)Normalized the index value and calculate the index weight based on network analysis method [28];
4)Making a comprehensive score on the vulnerability of auxiliary power book according to the comprehensive score and single index Identify the weak points and carry out risk early warning and visualization.nt update schemes.
Application examples
A. Key technology verification
During the development of the system, the auxiliary power of power plant A is taken as an example for technical verification. Fault data, operation and maintenance data and working condition data from January 2019 to December 2020 in 52 auxiliary power plants are used to test the correlation analysis and fault risk level prediction function.The test of weak point identification function is realized by using relevant data of power plant B auxiliary power in 2020 summer university operation mode.
1) Correlation analysis of fault characteristics.After data integration and cleaning, 10initial fault characteristics F1 ~ F10 are obtained, as shown in table B1 .By using the improved Relief F algorithm, the feature set {F2, F10, F1, F5, F3, F4, F9,} is further obtained, and the correlation degree of F5, F3 and F4 is 0.903, 0.862 and 0.888 respectively. Through the correlation analysis module, the fault features F6, F7, F8 with less influence are screened out, and the redundant features F3 and F4 with high correlation degree are eliminated. Finally, the optimal feature set of auxiliary power fault risk is determined as {F2, F10, F1, F5, F9}.
TABLE I
Initial characteristics of auxiliary power fault
label
|
Feature name
|
label
|
Feature name
|
f1
|
Time scale
|
F6
|
Single phase short circuit
|
f2
|
Layout classification
|
F7
|
Three phase short circuit fault
|
f3
|
Overload
|
F8
|
Fast switching of auxiliary power
|
f4
|
Over current
|
F9
|
Equipment failure
|
f5
|
Low voltage
|
F10
|
Automatic alarm
|
Prediction of failure risk level.The failure risk prediction module uses neural network algorithm, SVM algorithm, and Ada-DT algorithm to predict the risk level. The accuracy of the prediction is shown in Table II .
TABLE II
Prediction comparison of fault risk level
Method
|
Index
|
Level 1
|
Level 2
|
Level 3
|
Comprehensive
|
Neural network algorithm
|
Accuracy rate%
|
75.9
|
86.1
|
70.8
|
78.9
|
SVM algorithm
|
Accuracy rate%
|
83.0
|
86.9
|
85.7
|
85.1
|
ADA DT algorithm
|
Accuracy rate%
|
92.9
|
90
|
86.4
|
90.4
|
Finally, the ADA DT algorithm with high index values is selected to train the fault risk level prediction model.The prediction results are shown in TABLE Ⅲ."Level 1" in table Ⅲ represents the general failure risk of auxiliary power; "level 2" represents the moderate failure risk of auxiliary power; "level 3" represents the severe failure risk of auxiliary power; "comprehensive" is the overall prediction accuracy of three types of samples. It can be seen from TABLE Ⅲ that the prediction accuracy of fault risk level of three types of samples reaches 88.3%.
TABLE III
Prediction results of service power failure risk leve
|
Actual risk level
|
The forecast is level 1
|
The forecast is level 2
|
The forecast is level 3
|
The prediction accuracy of this grade
|
comprehensive
|
2017year52 weeks
|
Level 1
|
84
|
3
|
0
|
91.3%
|
88.3%
|
Level 2
|
83
|
86.9
|
85.7
|
85.3%
|
Level 3
|
92.9
|
90.0
|
86.4
|
72.7%
|
2) Identification of weak points of auxiliary power. The topology of power plant A is shown inTABLE Ⅳ. Since the nodes numbered 1 and 2 in the actual auxiliary power system are located at key positions, that is, the power node, only weak point identification verification except for nodes 1 and 2 is performed. The results of the weak point identification of auxiliary power in this area are shown in Table Ⅳ Shown. The analysis shows that through the weak point identification module, the ranking of the weak points under each single indicator and the top ten weak points in the comprehensive score are as follows: 1, 2, 4, 7, 5, 33, 37, 17, 45, 24.
TABLE IV
Weak point identification
sort
|
Single index node
|
Identification results of integrated method
|
1
|
A1
|
A2
|
A3
|
A4
|
Node number
|
Comprehensive score
|
2
|
4
|
4
|
4
|
4
|
4
|
86.7606
|
3
|
7
|
7
|
41
|
7
|
7
|
68.9339
|
4
|
5
|
5
|
7
|
5
|
8
|
47.7150
|
5
|
37
|
33
|
35
|
33
|
9
|
44.5756
|
6
|
33
|
37
|
33
|
35
|
37
|
39.2960
|
7
|
45
|
17
|
37
|
21
|
17
|
28.8858
|
8
|
45
|
17
|
37
|
21
|
17
|
28.8721
|
9
|
54
|
24
|
17
|
45
|
45
|
26.2468
|
10
|
57
|
26
|
45
|
57
|
24
|
23.7304
|
11
|
16
|
8
|
24
|
26
|
25
|
23.7305
|
A1 stands for network elasticity index ;A2 represents the risk of node voltage;A3 represents the risk of branch road change;A4 represents the risk of branch road change;
|
B. System application effectiveness
The auxiliary power operation analysis system has been applied in a power plant. By analyzing the massive operation data of auxiliary power, the main influencing factors of the fault are determined, and the fault risk level is predicted for the auxiliary power of the power plant. It provides technical support for the scientific development of emergency repair plan, identifies and manages more than 500 weak links of auxiliary power in advance, and avoids direct economic loss. The identification and analysis of system weak points is applied to the auxiliary power of power plant A. through the analysis of the selected auxiliary power system, the names of the identified weak equipment are sorted and visualized to assist the staff to formulate the maintenance plan.