A composite weighted human learning network and its application for modeling of the intermediate point temperature in USC.

Ultra supercritical power plant (USC) is a complex system associated with nonlinearity, uncertainties and multivariable couplings. Generally, it is difficult to build an accurate model to approximate the dynamic behavior of USC. This paper presents a novel composite weighted human learning optimization network (CWHLO) to tackle the above-mentioned problem. Firstly, by fully using of the statistic characteristic of the history operating data, K-means clustering algorithm is applied to partition the raw date, which extremely reduces the operating nonlinearity. Then, an improved real-coded human learning optimization (HLO) is adopted to built linear models in local regions. Different from conventional methods, the advantage of the proposed CWHLO is that the nonlinear model of the object is effectively replaced by a real-time dynamic linear model, which is more suitable for other control methods. Finally, the CWHLO model is compared with the traditional recursive least square method (RLS), and four other meta-heuristic algorithms, to show the advantages in approximating the dynamic behavior of USC. • By partitioning, the nonlinearity of the target is diluted to an acceptable range. • By using a compensation mechanism, the linearity of the obtained model is ensured. • Through iterative learning of HLO, reliable local model parameters are obtained. • By fuzzy fusion of local models, an accurate model of the whole system is obtained. [ABSTRACT FROM AUTHOR]