Manuel Berenguel

Fuzzy logic control of a solar power plant

This paper presents an application of fuzzy logic control to the distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong perturbations in the process. A special subclass of fuzzy inference systems, the TP (triangular partition) and TPE (triangular partition with evenly spaced midpoints) systems, is used to obtain adequate control signals in the whole range of possible operating conditions. The fuzzy logic controller has been tested in the real plant and results obtained are shown. A comparison with other control approaches widely used in the plant is performed using a nonlinear computer model of the field.

ROBUST ADAPTIVE MODEL PREDICTIVE CONTROL OF A SOLAR PLANT WITH BOUNDED UNCERTAINTIES

This paper presents the application of a robust adaptive control scheme to a distributed collector field of a solar power plant. The major characteristic of a solar power plant is that the primary energy source, solar radiation, cannot be manipulated. Solar radiation varies throughout the day, causing changes in plant dynamics and strong disturbances in the process. The controller uses a robust identification mechanism combined with a finite horizon receding controller to cope with the process dynamics having bounded uncertainties. The controller has been tested using a proven non-linear computer model of the field. Results obtained in the real plant are also shown.

Application of a gain scheduling generalized predictive controller to a solar power plant

Abstract This paper presents a gain-scheduling generalized predictive controller for the distributed collector field of a solar power plant. The controller has been developed by considering CARIMA models of the field, calculated taking into account the frequency response of the plant. Aspects of the modelling, problems encountered and performance of the control scheme are dealt with and illustrated by plant results. A proven non-linear computer model of the collector field has been used to check the controller behaviour before its real implementation, and some simulation results are shown too.

Feedback linearization control for a distributed solar collector field

This article describes the application of a feedback linearization technique for control of a distributed solar collector field using the energy from solar radiation to heat a fluid. The control target is to track an outlet temperature reference by manipulating the fluid flow rate through the solar field, while attenuating the effect of disturbances (mainly radiation and inlet temperature). The proposed control scheme is very easy to implement, as it uses a numerical approximation of the transport delay and a modification of the classical control scheme to improve startup in such a way that results compared with other control structures under similar conditions are improved while preserving short commissioning times. Experiments in the real plant are also described, demonstrating how operation can be started up efficiently.

Solar Energy Fundamentals

The use of renewable energy, such as solar energy, experienced a great impulse during the second half of the 1970s just after the first big oil crisis. At that time, economic issues were the most important factor and so interest in these types of processes decreased when oil prices fell. There is renewed interest in the use of renewable energies nowadays, driven by the need of reducing the high environmental impact produced by the use of fossil energy systems.

Fuzzy predictive control of a solar power plant

This work presents the application of fuzzy predictive control to a solar power plant. The proposed predictive controller uses fuzzy characterization of goals and constraints, based on the fuzzy optimization framework for multi-objective satisfaction problems. This approach enhances model based predictive control (MBPC) allowing the specification of more complex requirements. A brief description of the solar power plant and its simulator is given. Basic concepts of predictive control and fuzzy predictive control are introduced. Two fuzzy predictive controllers using different membership functions are designed for a solar power plant, and they are compared with a classical predictive controller. The simulation results show that the fuzzy MBPC formulation, based on a well proven successful algorithm, gives a greater flexibility to characterize the goals and constraints than classical control.

Interactive learning modules for PID control [Lecture Notes]

This article describes three interactive learning modules that are designed to develop intuition as well as a working knowledge of proportional-integral-derivative (PID) control. These three modules comprise a package called interactive learning modules for PID (ILM-PID). By illustrating concepts such as tuning, robustness, loop shaping, and antiwindup, ILM-PID can be used for demonstrations, exercises, and self-study. In this article we use interactive graphics to learn PID control and develop intuition.

Direct steam generation in solar boilers

This article describes the DISS facility, followed by an explanation of the main control problem. The control schemes for one of the three operating modes, the once-through mode, are then discussed, including system models, feedforward blocks, and PI blocks. Finally, experimental results and conclusion are presented.

Neural identification applied to predictive control of a solar plant

Abstract This paper presents an application of the general identification methodology to obtain neural predictors for use in a nonlinear predictive control scheme derived from the generalized predictive controller (GPC) structure. Every step of the design procedure is illustrated using a real-life problem: the identification of a solar power plant for predictive control. Different solutions are discussed for the problems that arise in the application of the methodology. Experimental results are given, showing the performance of the predictor and the controller.

Adaptive generalized predictive control of a distributed collector field

This brief paper presents an adaptive generalized predictive controller for the distributed collector field of a solar plant. The controller has been implemented using a method which avoids the heavy computation requirement of this type of controller. The method can he applied to processes that can be modeled by the reaction curve method, that is, a wide range of processes in industry. The results obtained in the plant are shown. >