Fault-tolerant nonlinear imc control

[ES] Este artículo trata sobre el control IMC no lineal y un método para hacerlo tolerante a los fallos en la planta. El control IMC no lineal se consigue por medio de modelos no lineales de la planta y de la inversa de la dinámica de la misma. Ambos se hacen mediante redes neuro-difusas del tipo ANFIS. La tolerancia a los fallos abruptos e incipientes en la planta se consigue mediante la adición de una señal de control compensadora. Ésta se calcula mediante una red neuronal que se entrena en línea a partir de la minimización del error de control. Se muestran resultados en simulación para una planta de control de pH.[EN] In this paper nonlinear IMC control and a method for achieving fault tolerant control in that framework is addressed. Nonlinear IMC control is attained by means of plant and plant-inverse nonlinear models. Both models are implemented using ANFIS neurofuzzy nets. Fault tolerance to abrupt and incipient faults is accomplished by adding a compensating control signal. This signal is computed using an online trained neural network. The training is performed minimising the control error. Simulation results in a pH control plant are presented.Este trabajo ha sido financiado por la Comisión Interministerial de Ciencia y Tecnología (CICYT) a través del proyecto DPI2006-15716-C02-02.Saludes Rodil, S.; Fuente, MJ. (2009). Control IMC No Lineal Tolerante a Fallos. Revista Iberoamericana de Automática e Informática industrial. 4(2):52-63. http://hdl.handle.net/10251/145970OJS52634

Flexibility Management with Virtual Batteries of Thermostatically Controlled Loads: Real-Time Control System and Potential in Spain

Load flexibility management is a promising approach to face the problem of balancing generation and demand in electrical grids. This problem is becoming increasingly difficult due to the variability of renewable energies. Thermostatically-controlled loads can be aggregated and managed by a virtual battery, and they provide a cost-effective and efficient alternative to physical storage systems to mitigate the inherent variability of renewable energy sources. However virtual batteries require that an accurate control system is capable of tracking frequency regulation signals with minimal error. A real-time control system allowing virtual batteries to accurately track frequency or power signals is developed. The performance of this controller is validated for a virtual battery composed of 1000 thermostatically-controlled loads. Using virtual batteries equipped with the developed controller, a study focused on residential thermostatically-controlled loads in Spain is performed. The results of the study quantify the potential of this technology in a country with different climate areas and provides insight about the feasibility of virtual batteries as enablers of electrical systems with high levels of penetration of renewable energy sources

Unsupervised Classification of Surface Defects in Wire Rod Production Obtained by Eddy Current Sensors

An unsupervised approach to classify surface defects in wire rod manufacturing is developed in this paper. The defects are extracted from an eddy current signal and classified using a clustering technique that uses the dynamic time warping distance as the dissimilarity measure. The new approach has been successfully tested using industrial data. It is shown that it outperforms other classification alternatives, such as the modified Fourier descriptors

Multi-objective optimization algorithms applied to residential building retrofitting at district scale: BRIOTOOL

The design of district and urban energy efficient retrofitting projects is a major challenge if contrasted solutions want to be implemented. From the establishment of the criteria to the calculation of indicators, there are several aspects to be considered, such as evaluating a series of refurbishment solutions or establishing an adequate method to select the optimal solution. Apart from this process requiring a high number of time and resources, it can result in a number of inaccuracies, leading to inadequate decisions or designs. The main achievement of the BRIOTOOL solution proposed is the transformation of a subjective problem (what the best combination of energy conservation measures to implement is) into a mathematical problem, which ensures a more robust decision-making process. In particular, by analysing which multi-objective optimization method (NSGA-II, IHS, MHACO or NSPSO) is the most appropriate, based on execution time, number of different and optimal solutions, and hypervolume of the Pareto front generated. As a result, the time reduction and the increase in the accuracy of the process compared to business as usual practices shows the benefits of the solution in designing energy efficient retrofitting projects at district level