Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

2022 ◽  
Vol 121 ◽  
pp. 105018
Author(s):  
Bangwu Dai ◽  
Fuli Wang ◽  
Yuqing Chang
Keyword(s):  
Data Mining ◽  
Power Plants ◽  
Economic Load Dispatch ◽  
Mining Technology ◽  
Multi Objective

Advances in Data Mining and Applications in Power Plants

2011 ◽  
Vol 347-353 ◽  
pp. 487-493
Author(s):  
Ang Bao ◽  
Wei Guo Pan ◽  
Wen Huan Wang
Keyword(s):  
Data Mining ◽  
Power Plants ◽  
Field Data ◽  
Thermal Power ◽  
Effective Means ◽  
Monitoring And Evaluation ◽  
Research Progress ◽  
Mining Technology ◽  
Equipment Operation ◽  
Real Time Database

Describes the theory and methods of data mining technology, and the latest research progress home and abroad. In the equipment operation of various thermal power plants, more and more field data is stored in the DCS real-time database, and there is always an abundance of knowledge hidden behind the data. Adopting the date mining technology to process and analyze these data can optimize the operation of power plants and provide effective means for monitoring and evaluation of the equipment.

Fuzzy multi-objective decision making approach for nuclear power plant installation

2020 ◽  
Vol 39 (5) ◽  
pp. 6339-6350
Author(s):  
Esra Çakır ◽  
Ziya Ulukan
Keyword(s):  
Linear Programming ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Energy Supply ◽  
Energy Demand ◽  
Nuclear Power ◽  
Power Plants ◽  
Total Duration ◽  
Objective Functions ◽  
Multi Objective

Due to the increase in energy demand, many countries suffer from energy poverty because of insufficient and expensive energy supply. Plans to use alternative power like nuclear power for electricity generation are being revived among developing countries. Decisions for installation of power plants need to be based on careful assessment of future energy supply and demand, economic and financial implications and requirements for technology transfer. Since the problem involves many vague parameters, a fuzzy model should be an appropriate approach for dealing with this problem. This study develops a Fuzzy Multi-Objective Linear Programming (FMOLP) model for solving the nuclear power plant installation problem in fuzzy environment. FMOLP approach is recommended for cases where the objective functions are imprecise and can only be stated within a certain threshold level. The proposed model attempts to minimize total duration time, total cost and maximize the total crash time of the installation project. By using FMOLP, the weighted additive technique can also be applied in order to transform the model into Fuzzy Multiple Weighted-Objective Linear Programming (FMWOLP) to control the objective values such that all decision makers target on each criterion can be met. The optimum solution with the achievement level for both of the models (FMOLP and FMWOLP) are compared with each other. FMWOLP results in better performance as the overall degree of satisfaction depends on the weight given to the objective functions. A numerical example demonstrates the feasibility of applying the proposed models to nuclear power plant installation problem.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

Framework and performance analysis of college English testing system based on data mining technology

2021 ◽  
pp. 1-11
Author(s):  
Liu Narengerile ◽  
Li Di ◽  
Keyword(s):  
Data Mining ◽  
Test System ◽  
Test Time ◽  
Testing System ◽  
Test Results ◽  
System Software ◽  
Examination System ◽  
Mining Technology ◽  
College English ◽  
And Performance

At present, the college English testing system has become an indispensable system in many universities. However, the English test system is not highly humanized due to problems such as unreasonable framework structure. This paper combines data mining technology to build a college English test framework. The college English test system software based on data mining mainly realizes the computer program to automatically generate test papers, set the test time to automatically judge the test takers’ test results, and give out results on the spot. The test takers log in to complete the test through the test system software. The examination system software solves the functions of printing test papers, arranging invigilation classrooms, invigilating teachers, invigilating process, collecting test papers, scoring and analyzing test papers in traditional examinations. Finally, this paper analyzes the performance of this paper through experimental research. The research results show that the system constructed in this paper has certain practical effects.

scholarly journals Mechanical equipment fault detection applying data mining technology

2020 ◽  
Vol 1684 ◽  
pp. 012024
Author(s):  
Yiqun Liu ◽  
Xiaogang Wang ◽  
Xiaoyuan Gong ◽  
Hua Mu
Keyword(s):  
Data Mining ◽  
Fault Detection ◽  
Mechanical Equipment ◽  
Mining Technology
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