Blind identification methods applied to Electricite de France's civil works and power plants monitoring

2002 ◽  
Author(s):  
G. D'Urso ◽  
P. Prieur ◽  
C. Vincent
Keyword(s):  
Power Plants ◽  
Blind Identification ◽  
Identification Methods

Nonlinear state affine identification methods: Applications to electrical power plants

Automatica ◽  
1984 ◽  
Vol 20 (2) ◽  
pp. 175-188 ◽  
Author(s):  
Henri Dang Van Mien ◽  
Dorothée Normand-Cyrot
Keyword(s):  
Power Plants ◽  
Electrical Power ◽  
Identification Methods ◽  
Nonlinear State

scholarly journals A review of second‐order blind identification methods

2021 ◽  
Author(s):  
Yan Pan ◽  
Markus Matilainen ◽  
Sara Taskinen ◽  
Klaus Nordhausen
Keyword(s):  
Second Order ◽  
Blind Identification ◽  
Identification Methods

scholarly journals Stacked Auto-Encoder Modeling of an Ultra-Supercritical Boiler-Turbine System

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4035 ◽  
Author(s):  
Zhang ◽  
Liu ◽  
Kong ◽  
Lee
Keyword(s):  
Power Plants ◽  
High Efficiency ◽  
Simulation Analysis ◽  
Measurement Data ◽  
Time Measurement ◽  
Multivariable System ◽  
Training Process ◽  
Identification Methods ◽  
Real Time Measurement ◽  
Multilayer Perceptron Network

The ultra-supercritical (USC) coal-fired boiler-turbine unit has been widely used in modern power plants due to its high efficiency and low emissions. Since it is a typical multivariable system with large inertia, severe nonlinearity, and strong coupling, building an accurate model of the system using traditional identification methods are almost impossible. In this paper, a deep neural network framework using stacked auto-encoders (SAEs) is presented as an effective way to model the USC unit. In the training process of SAE, maximum correntropy is chosen as the loss function, since it can effectively alleviate the influence of the outliers existing in USC unit data. The SAE model is trained and validated using the real-time measurement data generated in the USC unit, and then compared with the traditional multilayer perceptron network. The results show that SAE has superiority both in forecasting the dynamic behavior as well as eliminating the influence of outliers. Therefore, it can be applicable for the simulation analysis of a 1000 MW USC unit.

Parametric identification of integrated model of a coal-fired boiler in a thermal power plant

2019 ◽  
Vol 234 (4) ◽  
pp. 520-533
Author(s):  
Sreepradha Chandrasekharan ◽  
Rames C Panda ◽  
Bhuvaneswari Natrajan Swaminathan ◽  
Atanu Panda ◽  
T Thyagarajan
Keyword(s):  
Power Plant ◽  
Comparative Study ◽  
Expectation Maximization ◽  
Power Plants ◽  
Thermal Power ◽  
Parametric Identification ◽  
Primary Objective ◽  
Least Square ◽  
Model Parameters ◽  
Identification Methods

Retrofit or replacement of few units in a subcritical facility may not only improve overall efficiency of conversion of energy in a power plant but also support sustainability issues. The primary objective of this article is to identify model parameters of a coal-fired integrated boiler and to present a comparative study on three different identification methods. This leads to select most suitable models that are applied for the developed model of the boiler of 210 MW coal-fired thermal power plants. The mathematical models of economizer, drum, and super-heater assembly are derived using mass balance and energy balance equations. The derived multi input–multi output model is then validated, and the model parameters are identified using three different identification methods namely nonlinear least square technique, maximum likelihood estimation, and expectation maximization algorithms. Identification of the plant model will essentially help to frame a good controller. In this article, parameter estimation has been carried out from real-time plant as it provides selective tool through quantitative comparative study of the three methods. The expectation maximization method has been found to provide suitable results compared to the other two methods. Model parameters of integrated boiler of a comprehensive structure have been obtained for the first time using expectation maximization method.

Toeplitz Structured Subspace for Multi-Channel Blind Identification Methods

2021 ◽  
pp. 108152
Author(s):  
Abdulmajid Lawal ◽  
Naveed Iqbal ◽  
Azzedine Zerguine ◽  
Qadri Mayyala ◽  
Karim Abed-Meraim
Keyword(s):  
Blind Identification ◽  
Identification Methods

Use of fractals to describe microstructures of porous thick-film platinum electrodes

1992 ◽  
Vol 50 (1) ◽  
pp. 314-315
Author(s):  
Steven D. Toteda
Keyword(s):  
Fractal Dimension ◽  
Power Plants ◽  
Electrical Response ◽  
Cubic Phase ◽  
Platinum Electrodes ◽  
Fine Grained ◽  
Impedance Response ◽  
Platinum Particles ◽  
Energy Surfaces ◽  
Highly Porous

Zirconia oxygen sensors, in such applications as power plants and automobiles, generally utilize platinum electrodes for the catalytic reaction of dissociating O2 at the surface. The microstructure of the platinum electrode defines the resulting electrical response. The electrode must be porous enough to allow the oxygen to reach the zirconia surface while still remaining electrically continuous. At low sintering temperatures, the platinum is highly porous and fine grained. The platinum particles sinter together as the firing temperatures are increased. As the sintering temperatures are raised even further, the surface of the platinum begins to facet with lower energy surfaces. These microstructural changes can be seen in Figures 1 and 2, but the goal of the work is to characterize the microstructure by its fractal dimension and then relate the fractal dimension to the electrical response. The sensors were fabricated from zirconia powder stabilized in the cubic phase with 8 mol% percent yttria. Each substrate was sintered for 14 hours at 1200°C. The resulting zirconia pellets, 13mm in diameter and 2mm in thickness, were roughly 97 to 98 percent of theoretical density. The Engelhard #6082 platinum paste was applied to the zirconia disks after they were mechanically polished ( diamond). The electrodes were then sintered at temperatures ranging from 600°C to 1000°C. Each sensor was tested to determine the impedance response from 1Hz to 5,000Hz. These frequencies correspond to the electrode at the test temperature of 600°C.

The Effects of Ion Implantation on the Surface Features of Inconel 600

1985 ◽  
Vol 43 ◽  
pp. 288-289
Author(s):  
John D. Rubio
Keyword(s):  
Grain Boundary ◽  
Ion Implantation ◽  
Nuclear Power ◽  
Power Plants ◽  
Surface Region ◽  
Selective Dissolution ◽  
Boundary Region ◽  
Near Surface ◽  
Inconel 600 ◽  
Steam Generator Tubing

The degradation of steam generator tubing at nuclear power plants has become an important problem for the electric utilities generating nuclear power. The material used for the tubing, Inconel 600, has been found to be succeptible to intergranular attack (IGA). IGA is the selective dissolution of material along its grain boundaries. The author believes that the sensitivity of Inconel 600 to IGA can be minimized by homogenizing the near-surface region using ion implantation. The collisions between the implanted ions and the atoms in the grain boundary region would displace the atoms and thus effectively smear the grain boundary.To determine the validity of this hypothesis, an Inconel 600 sample was implanted with 100kV N2+ ions to a dose of 1x1016 ions/cm2 and electrolytically etched in a 5% Nital solution at 5V for 20 seconds. The etched sample was then examined using a JEOL JSM25S scanning electron microscope.

Work structure in nuclear power plants

1983 ◽  
Author(s):  
Marjorie B. Bauman ◽  
Richard F. Pain ◽  
Harold P. Van Cott ◽  
Margery K. Davidson
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Work Structure
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