An adaptive Fuzzy logic-based approach to PID control of steam turbines in solar applications. (1st October 2018) - Dettori, S.; Iannino, V.; Colla, V.; Signorini, A.
Highlights: An adaptive Fuzzy Logic PID approach is proposed to control steam turbines. A CSPP has been modeled focusing on power loop with variable steam conditions. Design of FPID controller through the knowledge acquired during simulation phase. FL allows to control steam turbines in off-design points with optimal performance. Control algorithm with reduced design and implementation time on PLC platforms. Abstract: In Concentrated Solar Power Plants, steam turbines controlled with standard Proportional Integrative Derivative (PID) methods may suffer from performance downgrading in power generation when the steam conditions deviate from nominal ones. An enhancement of standard steam turbine controller can be the key to achieve optimal performance also in non-nominal steam conditions. This paper presents the improvement of the PID control concept by exploiting Fuzzy Logic, an artificial intelligence technique that allows taking into account the human experience and knowledge on the system behavior. A real Concentrated Solar Power Plant has been modeled focusing on generated power control loop, its stability and performance analysis, knowledge useful to design a Fuzzy Inference System. A fuzzy logic controller is proposed to continuously adapt the PID parameters, to improve the steam turbine governor action. Its performance is compared to the classical PID tuned according to three different approaches. The fuzzy logic PID controller extends the simplicity of PID and adapts the control action to actual operating condition by providing the system with a sort of "decision-making skill". The possibility to design implementable algorithms on a Programmable Logic Controller, which have stringent computational speed and memory requirements, has been explicitly taken into account in the developed work, through the minimization of the controller complexity with a reduced number of fuzzy sets and fuzzy rules within the fuzzy inference system.