Using dynamical uncertainty models for estimation of uncertainty bounds on power plant performance prediction

2007 ◽  
Author(s):  
P.F. Odgaard ◽  
J. Stoustrup ◽  
B. Mataji
Keyword(s):  
Power Plant ◽  
Performance Prediction ◽  
Plant Performance ◽  
Dynamical Uncertainty ◽  
Uncertainty Models ◽  
Estimation Of Uncertainty

scholarly journals Data Driven Modelling of Nuclear Power Plant Performance Data as Finite State Machines

Modelling ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
Kshirasagar Naik ◽  
Mahesh D. Pandey ◽  
Anannya Panda ◽  
Abdurhman Albasir ◽  
Kunal Taneja
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Finite State Machine ◽  
State Machine ◽  
Plant Performance ◽  
Series Data ◽  
Cyberphysical Systems ◽  
Finite State ◽  
Machine Representation

Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.

Performance Testing of Coal Fired Power Plants

2007 ◽  
Author(s):  
Shane E. Powers ◽  
William C. Wood
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Performance Test ◽  
Model Development ◽  
Performance Testing ◽  
The United States ◽  
Plant Performance ◽  
Cycle Performance ◽  
Test Program

With the renewed interest in the construction of coal-fired power plants in the United States, there has also been an increased interest in the methodology used to calculate/determine the overall performance of a coal fired power plant. This methodology is detailed in the ASME PTC 46 (1996) Code, which provides an excellent framework for determining the power output and heat rate of coal fired power plants. Unfortunately, the power industry has been slow to adopt this methodology, in part because of the lack of some details in the Code regarding the planning needed to design a performance test program for the determination of coal fired power plant performance. This paper will expand on the ASME PTC 46 (1996) Code by discussing key concepts that need to be addressed when planning an overall plant performance test of a coal fired power plant. The most difficult aspect of calculating coal fired power plant performance is integrating the calculation of boiler performance with the calculation of turbine cycle performance and other balance of plant aspects. If proper planning of the performance test is not performed, the integration of boiler and turbine data will result in a test result that does not accurately reflect the true performance of the overall plant. This planning must start very early in the development of the test program, and be implemented in all stages of the test program design. This paper will address the necessary planning of the test program, including: • Determination of Actual Plant Performance. • Selection of a Test Goal. • Development of the Basic Correction Algorithm. • Designing a Plant Model. • Development of Correction Curves. • Operation of the Power Plant during the Test. All nomenclature in this paper utilizes the ASME PTC 46 definitions for the calculation and correction of plant performance.

scholarly journals Derivation of power loss factors to evaluate the impact of postcombustion CO2 capture processes on steam power plant performance

2011 ◽  
Vol 4 ◽  
pp. 1385-1394 ◽  
Author(s):  
Sebastian Linnenberg ◽  
Ulrich Liebenthal ◽  
Jochen Oexmann ◽  
Alfons Kather
Keyword(s):  
Power Plant ◽  
Co2 Capture ◽  
Power Loss ◽  
Plant Performance ◽  
Steam Power ◽  
Steam Power Plant ◽  
The Impact ◽  
Loss Factors

Matlab graphical user interface (GUI) code for solar tower power plant performance calculations

2021 ◽  
Vol 21 (1) ◽  
pp. 8-14
Author(s):  
Ihsan N. Jawad ◽  
Qais A. Rishack ◽  
Hussien S. Sultan
Keyword(s):  
User Interface ◽  
Power Plant ◽  
Solar Radiation ◽  
Output Power ◽  
Graphical User Interface ◽  
Operating Conditions ◽  
Plant Performance ◽  
Turbine Efficiency ◽  
Mass Flowrate ◽  
Geometrical Dimensions

In the present research, a Matlab program with a graphical user interface (GUI) has been established for studying the performance of a solar tower power plant (STPP). The program gives the ability for predicting the performance of STPP for different tower dimensions, ambient operating conditions and locations. The program is based on the solution of a mathematical model derived from the heat and mass balance for the tower components. The GUI program inputs are; tower dimensions, solar radiation, ambient temperature, pressure, wind velocity, turbine efficiency, emissivity and absorptivity for collector and ground and thermal conductivity and thickness for ground. However, the GUI program outputs are; temperature and pressure differences across the collector and tower, velocity in the tower, density of air in collector outlet, mass flowrate of air, efficiency for collector and tower, the overall efficiency and output power of STPP. The effect of the geometrical dimensions of STPP and some climatic variables on the plant performance was also studied. The results show that the output power increases with increasing the collector diameter, chimney diameter and solar radiation by an increasing of 0.282 kW/m, 0.204 kW/m and 0.046 kW/(W/m2) respectively.

Sign in / Sign up

Export Citation Format

Share Document