Method for Determining Energy-Consumption Benchmark State in the Thermal System of Coal-Fired Units Based on Hybrid Model

2014 ◽  
Vol 654 ◽  
pp. 93-96
Author(s):  
Long Fei Zhu ◽  
Ning Ling Wang ◽  
Peng Fu ◽  
Zhi Ping Yang
Keyword(s):  
Energy Consumption ◽  
Hybrid Model ◽  
Working Conditions ◽  
Thermal Power ◽  
Thermal System ◽  
Mechanism Analysis ◽  
Heat Transfer Characteristics ◽  
Operation Optimization ◽  
Operation Conditions ◽  
New Challenges

Considering the varying operation conditions and ambient constraints, the in-depth energy conservation of thermal power units is confronting new challenges. Based on the already made ‘energy-consumption benchmark state’ concept, the description of energy-consumption benchmark state was obtained in this paper to describe the economic performance of coal-fired power thermal system with the varying operation boundary, operation conditions and equipment performance. Breaking the limitations of traditional modelling which always make statistic analysis and mechanism analysis isolate, hybrid modeling method synthesizing the merit of the mechanism analysis and statistical method was proposed. Considering the heat transfer characteristics of thermal system, this model make the energy-consumption of unit correspondence with parameter sets of thermal system. Optimized parameter sets were gained with the fuel specific consumption setting as the optimization objective, thus obtain the energy-consumption benchmark state in thermal system of coal-fired units. The results show that the method for determining energy-consumption benchmark state in the thermal system of coal-fired units based on hybrid model makes significant reference for the energy-saving diagnosis and operation optimization of thermal power units under overall working conditions.

Mechanism of Energy-Consumption Benchmark State for Coal-Fired Power Units with Varying Boundary

2013 ◽  
Vol 860-863 ◽  
pp. 690-695
Author(s):  
Yong Zhang ◽  
Peng Fu ◽  
Ning Ling Wang
Keyword(s):  
Boundary Condition ◽  
Energy Consumption ◽  
Working Conditions ◽  
Economic Performance ◽  
Thermal Power ◽  
Operation Optimization ◽  
Operation Conditions ◽  
Optimal Values ◽  
New Challenges ◽  
Equipment Performance

The in-depth energy conservation of thermal power units is confronting new challenges under the varying operation conditions and ambient constraints. Compared with traditional optimal values, the description of energy-consumption benchmark state was proposed to describe the economic performance of thermal power units with the varying operation boundary, operation conditions and equipment performance. The energy consumption interactions of units were divided into 4 parts: parameters, equipment, subsystems and units. The models for energy-consumption benchmark states were established with the fuel specific consumption (FSC) setting as the optimization objective. Such a method was performed on a 600MW supercritical power unit and the results show that the energy-consumption benchmark state, which is related with the varying boundary, can reflect the boundary condition, operation lever and equipment performance. It makes significant reference for the energy-saving diagnosis and operation optimization of thermal power units under overall working conditions.

Operation Optimization Approaches of Thermal Power Units with Big Data-Driven Hybrid Modeling

2014 ◽  
Vol 631-632 ◽  
pp. 362-366
Author(s):  
Ning Ling Wang ◽  
Yong Zhang ◽  
Long Fei Zhu ◽  
Zhi Ping Yang
Keyword(s):  
Energy Consumption ◽  
Thermal Power ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Fuzzy Rough Set ◽  
Operation Optimization ◽  
Operation Conditions ◽  
Energy Consumption Model ◽  
Practical Guidelines ◽  
Data Driven Modeling

An accurate and reliable energy-consumption model is the key to operation optimization and energy-saving diagnosis of thermal power units especially under different operation conditions and boundaries. Conventional mathematical and data-driven modeling methods were overviewed and compared in this paper. A hybrid modeling based on thermodynamic theory and fuzzy rough set (FRS) method was proposed to process the great volume of operation data and describe the energy-consumption behavior of thermal power units. On this basis, the operation optimization was performed with intelligent computation methods to derive the realizable benchmark state with the whole set of operation parameters. The resultant optimum operation state reflects the exterior factors and system behavior, taking practical guidelines for the modeling and optimization of large thermal power units.

Intelligent Energy-Saving Decision Making and Delicacy Management for Power Enterprises

2014 ◽  
Vol 631-632 ◽  
pp. 1282-1286
Author(s):  
Yong Zhang ◽  
Ning Ling Wang
Keyword(s):  
Decision Making ◽  
Energy Saving ◽  
Management System ◽  
Thermal Power ◽  
Big Data Analytics ◽  
Fuzzy Rough Set ◽  
Operation Optimization ◽  
Operation Conditions ◽  
Performance Indexes ◽  
Decision Making Model

Energy-saving management is playing increasingly important parts in the energy conservation of thermal power generation. The economic performance indexes were decomposed and clarified to set a delicacy energy-saving management system. With the great volume of operation data, an fuzzy rough set (FRS) –based big data analytics were introduced to build the intelligent energy-saving decision-making model. Based on such energy-saving management system, the operation optimization practice was performed on a 600MW thermal power unit to determine the optimum working state under specific operation conditions. The result shows that the proposed energy-saving management can makes great guidelines for the operation optimization and energy-saving diagnosis of thermal power units.

scholarly journals Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions

2021 ◽  
Vol 9 ◽  
Author(s):  
Yujun Su ◽  
Mingyao Zou ◽  
Cheng Jiang ◽  
Hong Qian
Keyword(s):  
Energy Consumption ◽  
Least Squares ◽  
Hybrid Model ◽  
Working Conditions ◽  
Recursive Least Squares ◽  
Compensation Model ◽  
Mechanism Model ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Least Squares Algorithm

As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the “old and new” data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial least squares algorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.

scholarly journals Adaptive Modeling and Operation Optimization for the Cold End System of Thermal Power Units based on Mechanism and Statistical Analysis

2020 ◽  
Vol 319 ◽  
pp. 01003
Author(s):  
Wei Jin ◽  
Shaojun Ren ◽  
Yunshan Dong ◽  
Fengqi Si ◽  
Ce Wang ◽  
...  
Keyword(s):  
Cooling Tower ◽  
Thermal Power ◽  
Coupled Model ◽  
Operating Mode ◽  
Mechanism Analysis ◽  
Operating Characteristics ◽  
Operation Optimization ◽  
Actual Operating ◽  
Operation Process ◽  
Experimental Trials

The operation optimization for the cold end system is an efficient means to improve the economy of steam turbine units. To compensate for the inadequacy of the traditional mechanism analysis utilized in obtaining actual operating characteristics of the cold end system, the prediction model of the exhaust pressure was established on the basis of mechanism analysis combined with data from the operation process. An online adaptive updating strategy was introduced to guarantee the modeling accuracy. A discrete model of the cooling tower outlet water temperature (CTOWT) was constructed based on the operation data partitioned into different groups according to the pump operating mode change (POMC). Combining the above two models, the coupled model of the cold end system was therefore obtained. A model-based operation optimization system was then implemented for the cold end system in a coal-fired power plant. Experimental trials authenticate that the optimization suggestions provided by the system can effectively enhance the benefit of power generation.

Big Data Analytics-Based Energy-Consumption Feature Selection of Large Thermal Power Units

2013 ◽  
Vol 860-863 ◽  
pp. 1862-1866 ◽  
Author(s):  
Ning Ling Wang ◽  
De Gang Chen ◽  
Yong Ping Yang
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Analytics ◽  
Thermal Power ◽  
Big Data Analytics ◽  
Boundary Constraints ◽  
Operation Conditions ◽  
Complex Nonlinear System ◽  
Key Features ◽  
Selection Of

Large coal-fired power unit is a complex nonlinear system with more uncertainty to address, evaluate and optimize. It is essential and difficult to determine the key features contributing to the energy consumption of power units, especially considering the varying boundary constraints, operation conditions and system characteristics. In this paper idea of big data analytics is employed to clean the historian operation data efficiently and select the key energy-consumption features with less information losses. The result shows that the resultant key features reflect the exterior factors and system behavior. It makes great reference for the modeling and optimization of large thermal power units.

The Analysis on the Tribological Properties of TPU/PVC Blends under Oil Lubrication

2013 ◽  
Vol 774-776 ◽  
pp. 94-98
Author(s):  
Dao Yuan Pan ◽  
Peng Peng Wu ◽  
Zhong Xue Gao ◽  
Yu Zeng Zhang
Keyword(s):  
Working Conditions ◽  
Friction And Wear ◽  
Oil Lubrication ◽  
Wear Properties ◽  
Operation Conditions ◽  
Hydraulic Steering ◽  
Pros And Cons ◽  
Friction And Wear Properties ◽  
Lubrication Conditions ◽  
Better Than

Based on actual working conditions and parameters of the hydraulic steering gear, the purpose is optimizing the rubber seal of steering gear by different rubbers mixing technology. Compare the five kinds of rubber with metal of the friction characteristics in dynamic fit, it can obtain a performance excellent rubber real in the specific operation conditions. And then improve the overall service life of the steering gear. It is first prepared the same hardness TPU and PVC and blends that the ratio is 3:7, 5:5and7:3 in this article. The pros and cons of the five rubbers are analyzed in friction and wear properties of the above experimental. The test curve of coefficient friction and wear with time has been done under different load at constant low speed. It determines TPU/PVC = 3:7 blends through friction and wear and wear mechanism of five rubbers with steel comparatively analyses, and the heat resistance and wear resistance of them are better than the other TPU/PVC blends and PVC under oil lubrication conditions.

scholarly journals Hybrid-Model-Based Deep Reinforcement Learning for Heating, Ventilation, and Air-Conditioning Control

2021 ◽  
Vol 8 ◽  
Author(s):  
Huan Zhao ◽  
Junhua Zhao ◽  
Ting Shu ◽  
Zibin Pan
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Hybrid Model ◽  
Learning Process ◽  
Energy Cost ◽  
Air Conditioning ◽  
Electricity Price ◽  
Learning Efficiency ◽  
Model Based ◽  
Model Free

Buildings account for a large proportion of the total energy consumption in many countries and almost half of the energy consumption is caused by the Heating, Ventilation, and air-conditioning (HVAC) systems. The model predictive control of HVAC is a complex task due to the dynamic property of the system and environment, such as temperature and electricity price. Deep reinforcement learning (DRL) is a model-free method that utilizes the “trial and error” mechanism to learn the optimal policy. However, the learning efficiency and learning cost are the main obstacles of the DRL method to practice. To overcome this problem, the hybrid-model-based DRL method is proposed for the HVAC control problem. Firstly, a specific MDPs is defined by considering the energy cost, temperature violation, and action violation. Then the hybrid-model-based DRL method is proposed, which utilizes both the knowledge-driven model and the data-driven model during the whole learning process. Finally, the protection mechanism and adjusting reward methods are used to further reduce the learning cost. The proposed method is tested in a simulation environment using the Australian Energy Market Operator (AEMO) electricity price data and New South Wales temperature data. Simulation results show that 1) the DRL method can reduce the energy cost while maintaining the temperature satisfactory compared to the short term MPC method; 2) the proposed method improves the learning efficiency and reduces the learning cost during the learning process compared to the model-free method.

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