Abstract
This paper presents the application of TSK (Tagaki-Sugeno-Kang) fuzzy modelling to a distributed collector field solar power plant which consists of endogenous (inlet oil temperature) and exogenous (solar radiation, ambient temperature) variables. Subtractive clustering has been applied which employs a maximum error bound in order to produce the number of fuzzy rules, best describe the solar plants nonlinear behaviour. During a typical day the power plant's partial differential equations describe the variations in solar radiation, ambient temperature, and inlet fluid temperature among others. However in order to improve the plant's behaviour the design of a suitable controller requires linear or nonlinear ordinary equations, which describe the plant's behaviour. Hence the fuzzy TSK modelling approach in combination with the subtractive clustering method can allow an accurate within bounds open loop plant analysis and simulation. The proposed technique has been validated from experimental data obtained from the solar power plant in Almeria-Spain.
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