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This paper proposes a compound feedforward and uncertainty and disturbance estimator (UDE) based control strategy for uncertain systems with Input/Output time delay. The conventional UDE is modified to accommodate the time-delay characteristics. The major aims and modifications of the new structure are: i) to avoid the negative effects of the measurement delay, the predicted process output is calculated...
This paper presents an approach for intelligent distributed control of power plants using the concept of multi-agent systems (MAS). Solving the problem of optimally controlling a power plant based on multiple objectives, such as minimizing pollution, maximizing equipment life, etc., and coordinating each of the involved tasks that must be performed in distributed environments is a challenge, which...
This paper presents the use of Pareto optimization techniques that was previously analyzed with small scale power plant models where results could be verified analytically. Further research has been conducted into the use of applying these techniques to large scale models and analyzing performance. These approaches have been verified to scale well to larger applications. Through the use of multi-objective...
An Inverse Dynamic Neuro-Controller (IDNC) is developed to improve the superheater steam temperature control of a 300MW boiler unit. A recurrent neural network was used for building the Inverse Dynamic Process Models (IDPMs) for the superheater system. Two inverse dynamic neural network (NN) models referring to the first-stage and the second-stage water-spray attemperators are constructed separately...
Practical fault diagnosis of a thermal system is very important in ensuring safe and reliable operation of a power plant. However, it is a difficult task due to the structural complexity of a thermal system, varying degree of severity of a fault, and the wide range of operation of the power generating unit. An artificial neural network combined with optimal zoom search is proposed in this paper for...
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