Hybrid intelligent system for detection of Soft-Switching mode and control of a boost converter

[EN] In this work, an intelligent control based on artificial intelligence is presented. This novel control strategy aims to ensure thata half-bridge boost converter operates in soft-switching mode. As first step, an analysis of the power circuit is done, presentingthe two possible operating modes: Hard- and Soft-Switching. Then, a hybrid intelligent model is implemented with the aim ofclassifying the converter operating mode. A clustering method and three different classification algorithms are implemented and thecomparison between their results is done. Moreover, the intelligent model is implemented in the control loop of the converter withthe aim of ensuring that the converter operates in Soft-switching mode.[ES] En este trabajo de investigación se presenta una estrategia de control inteligente implementada en un convertidor elevador con topología de medio puente. El sistema se usa para asegurar que el convertidor funcione en modo "Soft-Switching". El primer paso es realizar el análisis del convertidor de potencia, mostrando los dos posibles modos de funcionamiento: "Hard-Switching" y "Soft-Switching". Posteriormente se implementa un modelo inteligente con el fin de identificar el modo de funcionamiento del convertidor. Este modelo se basa en un algoritmo de clasificación mediante técnicas inteligentes que es capaz de diferenciar entre los dos modos de funcionamiento. Se han obtenido muy buenos resultados de clasificación y una alta precisión, permitiendo la implementación del modelo en la estrategia de control del convertidor. La implementacion de este sistema permite asegurar  que el convertidor funcione en el modo deseado: modo "Soft-Switching".El CITIC, como Centro de Investigación del Sistema Universitario de Galicia, esta financiado por la Conselleria de Educación, Universidade e Formación Profesional de la Xunta de Galicia a través del Fondo Europeo de Desarrollo Regional (FEDER) y la Secretaria Xeral de Universidades (Ref.ED431G2019 / 01).Fernandez-Serantes, LA.; Casteleiro-Roca, JL.; Calvo-Rolle, JL. (2022). Sistema híbrido inteligente para el control y operación de un convertidor elevador en modo Soft-Switching. Revista Iberoamericana de Automática e Informática industrial. 19(4):356-368. https://doi.org/10.4995/riai.2022.16656OJS35636819

A novel method for anomaly detection using beta hebbian learning and principal component analysis

In this research work a novel two-step system for anomaly detection is presented and tested over several real datasets. In the first step the novel Exploratory Projection Pursuit, Beta Hebbian Learning algorithm, is applied over each dataset, either to reduce the dimensionality of the original dataset or to face nonlinear datasets by generating a new subspace of the original dataset with lower, or even higher, dimensionality selecting the right activation function. Finally, in the second step Principal Component Analysis anomaly detection is applied to the new subspace to detect the anomalies and improve its classification capabilities. This new approach has been tested over several different real datasets, in terms of number of variables, number of samples and number of anomalies. In almost all cases, the novel approach obtained better results in terms of area under the curve with similar standard deviation values. In case of computational cost, this improvement is only remarkable when complexity of the dataset in terms of number of variables is high.CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund and the Secretaría Xeral de Universidades (ref. ED431G 2019/01).info:eu-repo/semantics/publishedVersio

A distributed topology for identifying anomalies in an industrial environment

The devastating consequences of climate change have resulted in the promotion of clean energies, being the wind energy the one with greater potential. This technology has been developed in recent years following different strategic plans, playing special attention to wind generation. In this sense, the use of bicomponent materials in wind generator blades and housings is a widely spread procedure. However, the great complexity of the process followed to obtain this kind of materials hinders the problem of detecting anomalous situations in the plant, due to sensors or actuators malfunctions. This has a direct impact on the features of the final product, with the corresponding influence in the durability and wind generator performance. In this context, the present work proposes the use of a distributed anomaly detection system to identify the source of the wrong operation. With this aim, five different one-class techniques are considered to detect deviations in three plant components located in a bicomponent mixing machine installation: the flow meter, the pressure sensor and the pump speed.CITIC, as a Research Center of the university System of Galicia, is funded by Conselleria de Education, Universidade e Formacion Profesional of the Xunta de Galicia through the European regional Development Fund (ERDF) and the Secretaria Xeral de Universidades (Ref. ED431G 2019/01)

An intelligent system for harmonic distortions detection in wind generator power electronic devices

The high concern about climate change has boosted the promotion of renewable energy systems, being the wind power one of the key generation possibilities in this field. In this context, with the aim of ensuring the energy efficiency, the present work deals with the fault detection in the power electronic circuits of a wind generator system placed in a bioclimatic house. To do so, different outliers that emulate harmonic distortion appearance are tested. To implement a system capable of detecting this anomalous situations, six different one-class techniques are used, whose performance is thoroughly analyzed, offering interesting performance.info:eu-repo/semantics/publishedVersio

Intelligent one-class classifiers for the development of an intrusion detection system: the MQTT case study

[Abstarct] The ever-increasing number of smart devices connected to the internet poses an unprecedented security challenge. This article presents the implementation of an Intrusion Detection System (IDS) based on the deployment of different one-class classifiers to prevent attacks over the Internet of Things (IoT) protocol Message Queuing Telemetry Transport (MQTT). The utilization of real data sets has allowed us to train the one-class algorithms, showing a remarkable performance in detecting attacks

Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.info:eu-repo/semantics/publishedVersio

Intelligent system for switching modes detection and classification of a half-bridge buck converter

The present research shows the implementation of a classification algorithm applied to power electronics with the aim of detection different operation modes. The analysis of a half-bridge buck converter is done, showing two different working state: hard-switching and soft-switching. A model based on classification methods through intelligence techniques is implemented. This intelligent model is able to differentiate between the two operation modes. Very good results were obtained and high accuracy is achieved with the proposed model.- (undefined

Hybrid intelligent model for classification of the boost converter switching operation

The application of a hybrid intelligent model is applied to a boost converter with the aim of detecting the switching operating mode of the converter. Thus, the boost converter has been analyzed and the both operating mode are explained, distinguishing between Hard-switching and Soft-switching modes. Then, the dataset is created out of the data obtained from simulation of the real circuit and, finally, the hybrid intelligent classification model is implemented. The proposed model is able to distinguish between the HS and SS operating modes with high accuracy.CITIC, as a Research Center of the University System of Galicia, is funded by Conselleria de Educacion, Universidade e Formacion Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaria Xeral de Universidades (Ref. ED431G 2019/01)

A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery