Jian-qiang Li

Research and Application of Data Mining Technique in Power Plant

As the development of electric industry, more and more real-time data is sent to databases by data acquisition system and large amounts of data are accumulated. Abundant knowledge exists in those historical data. It is meaning to analyze those historical data in electric industry and find useful knowledge and rules from the mass of data to provide better decision support and better adjustment guidance. The concept and steps of data mining is introduced in particular. Based on the characteristic of electric data, the data mining technique is introduced into the electric industry and the feasibility and necessity are discussed. The application of data mining in electric power industrial is discussed. The fault diagnosis and operation optimization based on data mining is researched in detail. The application of data mining in electric industry can guide the optimal operation based on historical data and improve the economic efficient in power plant.

Application of neural network model combining information entropy and ant colony clustering theory for short-term load forecasting

This paper presented a hybrid neural network model to integrate information entropy theory and ant colony clustering for load forecasting. As short-term load forecasting is a complex problem with multifactor in power system, if all these factors are used as inputs of neural network, it will not only result in complicated network structure, but also long learning time and inaccurate prediction. First, information entropy theory is used to select relevant ones from all load influential factors, the results are used as inputs of neural network. It can reduce irrelevant load influential factors and the input variables of the input layer for neural network. Next, considering the features of power load and reduced influential factors, using ant colony clustering method, the practical historical load data within one year is divided into several groups. A separate module based on neural networks models each group. Then, the typical samples in each clustered group were selected as the training set for the separate improved Elman neural network which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. According to the procedures, the reduced input variables and the typical training samples for each neural network can be gotten. Thus the neural network forecasting model based on information entropy and ant colony clustering can be constructed which can effectively reduce the training time and improve convergent speed. During the forecasting process, pattern recognizing is employed to activate the corresponding module for hourly load forecasting. The presented model was tested using Hebei Province daily load data, and the satisfactory results were obtained.

The Application of Data Mining in Electric Short-Term Load Forecasting

Load forecasting is very important for electrical companies since it permits the system operator, the planning, the distribution and the control of the electrical energy supplied to customers. Electric load is affected by many uncertain factors, so many factors should be considered in forecasting process. This paper introduced a new method based on data mining to reflect the influence of weather factor on load. This method constructed model by decision tree and used it to make short-term forecasting. During the construction of decision tree, test attributes were sorted by the principle of maximum information plus which can reduce the complexity of decision tree. Statistical analysis of history data of Tianjin indicates that this method can improve the precision of forecasting and is effective and practical.

The research and application of data mining in power plant operation optimization

As the development of electric industry, more and more real-time data are sent to database by DAS and DCS and large amounts of data are accumulated. Abundantly valuable knowledge exists in the history data and it is hard to find and summarize them in traditional way. This paper proposes the operation optimization based on data mining in power plant. The basic structure of the operation optimization based on data mining is established and the fuzzy association rule mining is introduced to find the optimization target value from quantitative values of the equipments in power plant industry process. Based on the history data of a 300MW unit, the optimal values of the operating parameters are found out by data mining techniques. The optimal values are provided to the operators to guide the operation online and the excellent performance is achieved in the power plant.

Online self-optimizing control of coal-fired boiler combustion system

Coal-fired boiler combustion system is a complex multiinput and multioutput plant with strong nonlinear and large time-delay. Whats more, it is difficult to establish accurate mathematical model. Because the oxygen content signal has large time-delay and air leak exists, traditional combustion control method that uses oxygen meter to decide air flow is inefficient. Aiming at common problems in the boiler combustion process and based on heat balance principle, this paper adopts the variable oxygen content correction project which sets optimal excess air ratio according to current combustion thermal efficiency and dynamic self-optimizing control which can self-search optimal combustion state. The air/fuel ratio can be adjusted on-line to maintain the optimal combustion condition. Local experiment proves that adopting dynamic self-optimizing control to search optimal air/fuel ratio in combustion process can improve boilers thermal efficiency and achieve the goal of optimal combustion.

Study on mathematical model of coordinated control system for supercritical units

In order to achieve the ability of primary frequency regulation better, the boiler-turbine coordinated control system for the large capacity unit has become an important research in thermal control field, using the mechanism mathematical model for reference, utilizing the method of pressure node, the dynamic simulation mathematic model of the coordinated control system for supercritical units is given, analyzing and computing the important parameters of the model, including the heat storage coefficient of the evaporating node and the superheating node. Lastly the model is simulated with MATLAB software, through the studies of the simulation curves, demonstrating the validity of the dynamic simulation mathematic model.

Energy Loss Analysis Based on Fuzzy Association Rule Mining in Power Plant

The rational determination of operation optimization values is very important to economical analysis and operation optimization in power plant. Based on the correlation of operation data, the economical analysis and operation optimization based on data mining was proposed to guide the operation optimization. The basic structure of economical analysis and operation optimization based on data mining was established and the fuzzy association rule mining was introduced to determine the operation optimization value. The deviation analysis and optimal operation guidance were carried out based on the optimal values obtained by data mining to guide the optimal operation in power plant. Experiment results show that the economical analysis and operation optimization based on data mining can guide the operator to make better adjustments and improve the economic efficiency greatly in power plant.

The Operation Optimization based on Correlation Analysis of Operation Parameters in Power Plant

Accurate operation data is the foundation of system identification and system analysis. Based on the characteristic of operation parameters in power plant, the method of data selection and data processing was discussed and the correlation analysis of operation parameters was introduced to operation optimization. The correlation coefficients were proposed to measure the related degree between operation parameters. The operation optimization values were determined by the results of correlation analysis and data mining, which is the foundation of economical analysis and operation optimization. The optimal values of the excess air coefficient were obtained and provided to the operators online to guide the operation based on the results of the data analysis. The experiment results show that the boiler efficiency is improved greatly. The determination of the optimization value based on operation data by correlation analysis and data mining provides a novel and efficient method for operation optimization in power plant.

The application of operation optimization decision support system based on data mining in power plant

The research and application of the operation optimization are very active now, but there are still some problems in this field, for example, the accurate process model is hard to establish for current complex industry process. So they can not instruct operation optimization very well. As more and more real-time data are sent and stored into database, abundantly valuable knowledge exists in the history data. This paper proposed the operation optimization decision support system based on data warehouse and data mining. The synthetic structure of the operation optimization DSS and the design scheme of its sub-systems are put forward and explicated, at the same time, the fuzzy association rule is introduced in this paper to mine the optimal operation value of a 300WM unit from large amount of history data in power plant. The optimal values are provided to the operators to guide the operation online and great success has been achieved in the industry process.

Research and application of data mining in power plant process control and optimization

As more and more real-time data is sent to databases by DAS, large amounts of data are accumulated in power plants. Abundant knowledge exists in historical data but it is hard to find and summarize this in a traditional way. This paper proposes a method of operation optimization based on data mining in a power plant. The basic structure of the operation optimization based on data mining is established and the improved fuzzy association rule mining is introduced to find the optimization values from the quantitative data in a power plant. Based on the historical data of a 300MW unit, the optimal values of the operating parameters are found by using data mining techniques. The optimal values are provided to guide the operation online and experiment results show that excellent performance is achieved in the power plant.