Advanced power plant start-up automation based on the integration of soft computing and hard computing techniques

Abstract

The conventional HC (Hard Computing) techniques, i.e., ES (Expert System) and DDC (Digital Direct Control), have so far played a key role in large-scale thermal power plant automation systems which are based on a hierarchy structure. In this paper, we propose to integrate these HC techniques with the emerging SC (Soft Computing) in order to achieve the next-generation optimal automation system. SC, implemented through the integration of reinforcement-learning-based NN (Neural Networks) and GA (Genetic Algorithms), is capable of stochastic searching, learning and generalization, in solving those online optimization problems that are highly non-linear and accompanied with local optima. During the start-up process, SC is applied to generate and search the optimal or near-optimal schedule for the ES, which in turn controls the DDC-based controllers and monitors the whole power plant process with the given schedule. In accordance with our previous research, it has been verified that the optimal or near-optimal schedule can be obtained within tens of seconds, a time range which should be acceptable in power plant operation. The optimal schedule reduces the start-up time by approximately 10% for warm start-up mode.