Abstract
To improve the unit load following capacity of a large-capacity coal-fired supercritical (SC) power unit under automatic generation control (AGC), an intelligent model predictive optimal control (MPOC) scheme is proposed. It employs a nonlinear autoregressive moving average (NARMA) neural network model as the prediction model of the energy conversion unit and a more efficient simplified particle swarm optimization (PSO) algorithm as the optimal solution search approach. The effectiveness of the proposed MPOC scheme is evaluated by extensive control simulation tests on a commercial-grade simulator of a 600MW SC power unit. It is shown that the proposed approach can greatly improve the load following speed of the supercritical energy conversion unit, and at the same time keeps main steam pressure within safety limits.
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