Highly nonlinear control of a solar thermal power plant using soft computing fuzzy tuning techniques

Abstract

Society is experiencing massive growth of global industrialised populations, which is putting increasing pressure on western governments to pursue more persuasive means to maintain and increase their share of the world’s diminishing fossil fuel reserves. To combat this, there is a growing body of enlightened researchers who are directing their abilities towards the development of alternative and preferably renewable energy types of supply systems. Many of these real world systems exhibit varying degrees of non-linearity. An example of this is the significant variations in the dynamic characteristics of a distributed collector field within a solar thermal power plant. Here a Sugeno-type fuzzy incremental controller was tuned using an ANFIS (Adaptive Neural Fuzzy Inference System) to optimise the fuzzy controller’s pre-clustered input membership functions, while a multiobjective genetic algorithm with an enhanced decision support system was used to fine tune the parameters of its first order output membership functions. The resulting solution choice produced an incremental fuzzy controller which was used to successfully control the plant exclusively in its high nonlinear regions, i.e., where the oil flow fell below 5 litres per second. This allowed the plant to function in environments where local solar radiation conditions have always been regarded as marginal. A feedforward term was also used to control plant disturbances caused by solar irradiation, mirror reflectivity etc.

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ORCID for A.J. Chipperfield: orcid.org/0000-0002-3026-9890

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