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HYBRID INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

ISMAIL ALNAIMI, FIRAS BASIM ISMAIL ALNAIMI (2010) HYBRID INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS. PhD thesis, UNIVERSITI TEKNOLOGI PETRONAS.

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Abstract

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been developed and coded within the MATLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant control system of an MNJ coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the developed artificial intelligent systems. The feed-forward NN methodology has been adopted as a major computational intelligent tool in both systems. The Root Mean Square Error has been widely used as a performance indicator of the developed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main NN topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general NN topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the developed systems before or viii at the same time as the plant control system. The second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal NN topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower Root Mean Square Error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure NN system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The developed artificial intelligent systems could be applied on-line as a reliable controller of the thermal power plant boiler.

Item Type: Thesis (PhD)
Academic Subject : Academic Department - Mechanical Engineering - Petroleum
Subject: UNSPECIFIED
Divisions: Engineering > Mechanical
Depositing User: Users 5 not found.
Date Deposited: 05 Jun 2012 08:28
Last Modified: 19 Jan 2017 15:46
URI: http://utpedia.utp.edu.my/id/eprint/2859

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