國立虎尾科技大學 |

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques./
作者:	Al-Sinbol, Ghassan Khalil Ismaeel.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	158 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10261963
ISBN:	9781369764253

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques.
Al-Sinbol, Ghassan Khalil Ismaeel.

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 158 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--West Virginia University, 2017.

This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.

ISBN: 9781369764253Subjects--Topical Terms:

561152
Engineering.

Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques.
LDR:04384nam a2200373 4500 001 890668
005 20180727091501.5
008 180907s2017 ||||||||||||||||| ||eng d
020 $a 9781369764253
035 $a (MiAaPQ)AAI10261963
035 $a (MiAaPQ)wvu:11543
035 $a AAI10261963
040 $a MiAaPQ $c MiAaPQ
100 1 $a Al-Sinbol, Ghassan Khalil Ismaeel. $3 1148503
245 1 0 $a Monitoring and Control Framework for Advanced Power Plant Systems Using Artificial Intelligence Techniques.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 158 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Mario G. Perhinschi.
502 $a Thesis (Ph.D.)--West Virginia University, 2017.
520 $a This dissertation presents the design, development, and simulation testing of a monitoring and control framework for dynamic systems using artificial intelligence techniques. A comprehensive monitoring and control system capable of detecting, identifying, evaluating, and accommodating various subsystem failures and upset conditions is presented. The system is developed by synergistically merging concepts inspired from the biological immune system with evolutionary optimization algorithms and adaptive control techniques.
520 $a The proposed methodology provides the tools for addressing the complexity and multi-dimensionality of the modern power plants in a comprehensive and integrated manner that classical approaches cannot achieve. Current approaches typically address abnormal condition (AC) detection of isolated subsystems of low complexity, affected by specific AC involving few features with limited identification capability. They do not attempt AC evaluation and mostly rely on control system robustness for accommodation. Addressing the problem of power plant monitoring and control under AC at this level of completeness has not yet been attempted.
520 $a Within the proposed framework, a novel algorithm, namely the partition of the universe, was developed for building the artificial immune system self. As compared to the clustering approach, the proposed approach is less computationally intensive and facilitates the use of full-dimensional self for system AC detection, identification, and evaluation. The approach is implemented in conjunction with a modified and improved dendritic cell algorithm. It allows for identifying the failed subsystems without previous training and is extended to address the AC evaluation using a novel approach.
520 $a The adaptive control laws are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions. Artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through numerical simulation.
520 $a This dissertation also presents the development of an interactive computational environment for the optimization of power plant control system using evolutionary techniques with immunity-inspired enhancements. Several algorithms mimicking mechanisms of the immune system of superior organisms, such as cloning, affinity-based selection, seeding, and vaccination are used. These algorithms are expected to enhance the computational effectiveness, improve convergence, and be more efficient in handling multiple local extrema, through an adequate balance between exploration and exploitation.
520 $a The monitoring and control framework formulated in this dissertation applies to a wide range of technical problems. The proposed methodology is demonstrated with promising results using a high validity DynsimRTM model of the acid gas removal unit that is part of the integrated gasification combined cycle power plant available at West Virginia University AVESTAR Center. The obtained results show that the proposed system is an efficient and valuable technique to be applied to a real world application. The implementation of this methodology can potentially have significant impacts on the operational safety of many complex systems.
590 $a School code: 0256.
650 4 $a Engineering. $3 561152
650 4 $a Mechanical engineering. $3 557493
650 4 $a Chemical engineering. $3 555952
690 $a 0537
690 $a 0548
690 $a 0542
710 2 $a West Virginia University. $b Statler College of Engineering & Mineral Resources. $3 1148504
773 0 $t Dissertation Abstracts International $g 78-11B(E).
790 $a 0256
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10261963