Applications of aerospace technology in the electric power industry

An overview of the electric power industry, selected NASA contributions to progress in the industry, linkages affecting the transfer and diffusion of technology, and, finally, a perspective on technology transfer issues are presented

Machine learning solutions for maintenance of power plants

The primary goal of this work is to present analysis of current market for predictive maintenance software solutions applicable to a generic coal/gas-fired thermal power plant, as well as to present a brief discussion on the related developments of the near future. This type of solutions is in essence an advanced condition monitoring technique, that is used to continuously monitor entire plants and detect sensor reading deviations via correlative calculations. This approach allows for malfunction forecasting well in advance to a malfunction itself and any possible unforeseen consequences. Predictive maintenance software solutions employ primitive artificial intelligence in the form of machine learning (ML) algorithms to provide early detection of signal deviation. Before analyzing existing ML based solutions, structure and theory behind the processes of coal/gas driven power plants is going to be discussed to emphasize the necessity of predictive maintenance for optimal and reliable operation. Subjects to be discussed are: basic theory (thermodynamics and electrodynamics), primary machinery types, automation systems and data transmission, typical faults and condition monitoring techniques that are also often used in tandem with ML. Additionally, the basic theory on the main machine learning techniques related to malfunction prediction is going to be briefly presented

Boiler Control Improving Efficiency of Boiler Systems

This master thesis is written at the division of Industrial Electrical Engineering and Automation (IEA) Lund University, Faculty of Engineering, in cooperation with AB Regin in Landskrona. AB Regin wishes to determine whether their existing controller platforms can be used to implement a control system for boilers. The control system should be flexible in terms of boiler size, boiler fuel and boiler type. The main aspects associated with the combustion process are reviewed, and different types of fuel commonly used in boilers are investigated and compared. The most common boiler designs are described, and definitions used in boiler context are explained. To establish what techniques have been developed to improve boiler efficiency an empirical study is performed. The different techniques are discussed in detail, involving both control systems and design changes. Finally the measurement instruments involved in these improvement techniques are described. Case studies are performed to find out which techniques are used in practice to improve efficiency of boiler systems. To get a good idea of common techniques,the cases include establishments that use different fuels, and range from small scale to large scale. An effort is made to implement a boiler controller on the EXOcompact platform from AB Regin. A control algorithm is outlined and implemented for a fictional system. The system is then simulated using a demo kit from Regin. Some tests are performed to verify that the function of the control system is acceptable, and the results are discussed. Finally the overall thesis is discussed, and issues that should be addressed in future development are listed and explained

Summary and recommendations on nuclear electric propulsion technology for the space exploration initiative

A project in Nuclear Electric Propulsion (NEP) technology is being established to develop the NEP technologies needed for advanced propulsion systems. A paced approach has been suggested which calls for progressive development of NEP component and subsystem level technologies. This approach will lead to major facility testing to achieve TRL-5 for megawatt NEP for SEI mission applications. This approach is designed to validate NEP power and propulsion technologies from kilowatt class to megawatt class ratings. Such a paced approach would have the benefit of achieving the development, testing, and flight of NEP systems in an evolutionary manner. This approach may also have the additional benefit of synergistic application with SEI extraterrestrial surface nuclear power applications

Steam Temperature Control Based on Modified Active Disturbance Rejection

Tato práce je zaměřena na studium využitelnosti algoritmu aktivního odstranění vlivu neměřené poruchy a modifikovaného algoritmu aktivního odstranění vlivu neměřené poruchy aplikovaného na řízení teploty přehřáté páry v tepelné elektrárně. Studie byly prováděny na základě linearizovaného modelu přehříváku. Studium algoritmu aktivního odstranění vlivu neměřené poruchy je relevantní v souvislosti s možností jeho aplikace pro komplexní technologické procesy (procesy/soustavy s velkým počtem parametrů). zde je studovaným objektem přehřívák, který je součástí technologického uzlu přípravy přehřáté páry pro dodávku vysokotlaké páry do vysokotlakého stupně turbíny. Efektivnost obou algoritmů aktivního odstranění vlivu poruchy v porovnání s klasickým PID regulátorem je demonstrována na výsledcích simulací. Podrobnější analýza obou metod je nezbytná zejména v případě, kdy řídíme systém vyššího řádu jako například v případě přehříváku. Výsledky analýzy jsou také v práci uvedeny.This work is aimed at studying the applicability of active disturbance rejection algorithm and modified active disturbance rejection algorithm for use in controlling the superheated steam temperature in propulsion of thermal power plant. The studies were conducted on the basis of the linearized model of the superheater. The algorithm itself for active disturbance rejection is relevant to study in connection with the possibility of its application for complex technological objects (objects with a large number of parameters). These objects are the superheater, which is part of the superheated steam preparation object, for supplying high-pressure steam to the turbine high pressure stage. To demonstrate the effectiveness of this algorithm (within the framework of the problem of disturbance rejection) in comparison with the classical PID controller, the results of mathematical modeling are presented. The paper also presents the results of a study of a modified active disturbance rejection method. The need to study this method is due to the high order of the mathematical model of the control object under study. The results of these studies are also given in the work

Artificial Intelligence Modeling-Based Optimization of an Industrial-Scale Steam Turbine for Moving toward Net-Zero in the Energy Sector

Augmentation of energy efficiency in the power generation systems can aid in decarbonizing the energy sector, which is also recognized by the International Energy Agency (IEA) as a solution to attain net-zero from the energy sector. With this reference, this article presents a framework incorporating artificial intelligence (AI) for improving the isentropic efficiency of a high-pressure (HP) steam turbine installed at a supercritical power plant. The data of the operating parameters taken from a supercritical 660 MW coal-fired power plant is well-distributed in the input and output spaces of the operating parameters. Based on hyperparameter tuning, two advanced AI modeling algorithms, i.e., artificial neural network (ANN) and support vector machine (SVM), are trained and, subsequently, validated. ANN, as turned out to be a better-performing model, is utilized to conduct the Monte Carlo technique-based sensitivity analysis toward the high-pressure (HP) turbine efficiency. Subsequently, the ANN model is deployed for evaluating the impact of individual or combination of operating parameters on the HP turbine efficiency under three real-power generation capacities of the power plant. The parametric study and nonlinear programming-based optimization techniques are applied to optimize the HP turbine efficiency. It is estimated that the HP turbine efficiency can be improved by 1.43, 5.09, and 3.40% as compared to that of the average values of input parameters for half-load, mid-load, and full-load power generation modes, respectively. The annual reduction in CO2 measuring 58.3, 123.5, and 70.8 kilo ton/year (kt/y) corresponds to half-load, mid-load, and full load, respectively, and noticeable mitigation of SO2, CH4, N2O, and Hg emissions is estimated for the three power generation modes of the power plant. The AI-based modeling and optimization analysis is conducted to enhance the operation excellence of the industrial-scale steam turbine that promotes higher-energy efficiency and contributes to the net-zero target from the energy sector

Development of a Solution for Start-up Optimization of a Thermal Power Plant

This thesis covers optimizing the first phase of the start-up of a thermal power plant using Nonlinear Model Predictive Control (NMPC) and state estimation using an Unscented Kalman Filter (UKF). The start-up has been optimized in regards to time and fuel usage. The thesis is done as a joint project between Vattenfall and Modelon. Both NMPC and UKF are nonlinear methods and require a model of the power plant. The model used in this thesis has been developed in the language Modelica in a previous master thesis and has been extended and improved upon during this thesis. The optimization and simulation of the model required by the NMPC and UKF was done within the framework of JModelica.org. Another, more detailed, model of the power plant, developed by Vattenfall, was originally planned to be used as the process to be controlled. State estimation using the UKF has been successful, with a maximum mean absolute error of 0.7 % when estimating the states of the detailed model in a reference startup. When using the NMPC to control the optimization model itself, the simulated start-up time is 70 minutes faster compared to a reference start-up using the detailed model. This is more than half the time of the first phase of the start-up. The total firing power, which relates to the fuel amount, is also considerably less, with the optimized value being about 40 % of that in the reference soft start with the detailed model. Due to difficulties in initializing the detailed model, it was not possible to run it online together with the NMPC and UKF. Running the NMPC and UKF together on the optimization model worked, but the NMPC failed to find an optimal trajectory 8 out of 10 iterations. The conclusion is that the start-up has potential for optimization, but requires more robust models to work with