Model-based Fault-tolerant Control to Guarantee the Performance of a Hybrid Wind-Diesel Power System in a Microgrid Configuration

AbstractThis paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its fault-tolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller

Robust nonlinear trajectory controllers for a single-rotor UAV with particle swarm optimization tuning

This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers.Peer ReviewedPostprint (published version

Unravelling the relevance of the polyadenylation factor EhCFIm25 in entamoeba histolytica through proteomic analysis

We recently reported that silencing of the polyadenylation factor EhCFIm25 in Entamoeba histolytica, the protozoan which causes human amoebiasis, affects trophozoite proliferation, death, and virulence, suggesting that EhCFIm25 may have potential as a new biochemical target. Here, we performed a shotgun proteomic analysis to identify modulated proteins that could explain this phenotype. Data are available via ProteomeXchange with identifier PXD027784. Our results revealed changes in the abundance of 75 proteins. Interestingly, STRING analysis, functional GO‐term annotations, KEGG analyses, and literature review showed that modulated proteins are mainly related to glycolysis and carbon metabolism, cytoskeleton dynamics, and parasite virulence, as well as gene expression and protein modifications. Further studies are needed to confirm the hypotheses emerging from this proteomic analysis, to thereby acquire a comprehensive view of the molecular mechanisms involved

Model-based fault-tolerant control to guarantee the performance of a hybrid wind-diesel power system in a microgrid configuration

This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5 seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its faulttolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller. © 2013 The Authors. Published by Elsevier B.V

Predictive control of a closed grinding circuit system in cement industry

This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller to manage distributed controllers installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller.This paper presents the development of a nonlinear model predictive controller (NMPC) applied to a closed grinding circuit system in the cement industry. A Markov chain model is used to characterize the cement grinding circuit by modeling the ball mill and the centrifugal dust separator. The probability matrices of the Markovian model are obtained through a combination of comminution principles and experimental data obtained from the particle size distribution of cement samples at specific stages of the system. The NMPC is designed as a supervisory controller to manage distributed controllers installed in the process. Both the model and the controller are validated online through the implementation of the proposed approach in the supervisory control and data acquisition (SCADA) system of an industrial plant. The results show a significant improvement in the performance of the grinding circuit in comparison to the operation of the system without the proposed controller

Localización de fuentes de contaminantes del aire basada en UAV utilizando métodos gradientes y probabilísticos

Este trabajo propone un algoritmo para la localización de la fuente de contaminantes del aire utilizando un vehículo aéreo no tripulado (UAV). El algoritmo combina una búsqueda basada en gradiente con un método probabilístico para localizar la fuente de contaminantes. El diseño del componente de búsqueda basado en gradiente se basa en el recocido simulado metaheurístico y permite rastrear el penacho de contaminante. El componente probabilístico contribuye a generar una posición heurística de la ubicación de origen, que es utilizada por el metaheurístico basado en gradiente para navegar hacia la posición de origen, reduciendo la región de búsqueda en cada tiempo de muestreo. El algoritmo propuesto se probó en un entorno contaminado simulado. Los resultados mostraron una alta efectividad y solidez de la estrategia propuesta. © 2018 IEEE.This work proposes an algorithm for air pollutant source localization using an Unmanned Aerial Vehicle (UAV). The algorithm combines a gradient-based search with a probabilistic method to localize the pollutant source. The design of the gradient-based search component is based on the simulated annealing metaheuristic and allows to trace the plume of pollutant. The probabilistic component contributes to generate a heuristic position of the source location, which is used by the gradient-based metaheuristic to navigate towards the source position, reducing the searching region at each sampling time. The proposed algorithm was tested in a simulated polluted environment. The results showed high effectiveness and robustness of the proposed strategy.Dalla

UAV-Based air pollutant source localization using combined metaheuristic and probabilistic methods

Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant's source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm.Air pollution is one of the greatest risks for the health of people. In recent years, platforms based on Unmanned Aerial Vehicles (UAVs) for the monitoring of pollution in the air have been studied to deal with this problem, due to several advantages, such as low-costs, security, multitask and ease of deployment. However, due to the limitations in the flying time of the UAVs, these platforms could perform monitoring tasks poorly if the mission is not executed with an adequate strategy and algorithm. Their application can be improved if the UAVs have the ability to perform autonomous monitoring of the areas with a high concentration of the pollutant, or even to locate the pollutant source. This work proposes an algorithm to locate an air pollutant’s source by using a UAV. The algorithm has two components: (i) a metaheuristic technique is used to trace the increasing gradient of the pollutant concentration, and (ii) a probabilistic component complements the method by concentrating the search in the most promising areas in the targeted environment. The metaheuristic technique has been selected from a simulation-based comparative analysis between some classical techniques. The probabilistic component uses the Bayesian methodology to build and update a probability map of the pollutant source location, with each new sensor information available, while the UAV navigates in the environment. The proposed solution was tested experimentally with a real quadrotor navigating in a virtual polluted environment. The results show the effectiveness and robustness of the algorithm