Power System Stabilizer based on Model Predictive Control

[EN] A model predictive power system stabilizer is proposed in this paper to damp power oscillations in an electric power system (EPS). The design of the stabilizer is optimal in the sense that its parameters are determined by using off-line particle swarm optimization (PSO) technique. The proposed methodology is applied to an EPS composed by a single machine connected to an infinite bus (SMIB). The analysis is performed through a small signal stability analysis, deriving incremental equations linearized around an operating point. The results obtained by the proposed method are compared with a conventional power system stabilizer, also optimized by PSO. Through numerous computer simulations under different operating conditions andperturbations on the SMIB, it was possible to establish some advantages of the proposed technique as compared with the conventional technique.[ES] Se propone un estabilizador de potencia predictivo para amortiguar oscilaciones de potencia en un sistema eléctrico de potencia(SEP) formado por una sola máquina conectada a una barra infinita (Single Machine Infinite Bus, SMIB). Este enfoque considera un análisis de estabilidad de pequeña señal, usando un modelo incremental alrededor de un punto de operación. El estabilizador proporciona señales de control óptimas, debido a que además de utilizar el controlador predictivo basado en modelo (Model Predictive Controller, MPC) sus parámetros se optimizan fuera de línea empleando un algoritmo de optimización por enjambre de partículas (Particle Swarm Optimization, PSO). Su comportamiento se compara con un estabilizador del sistema potencia convencional, con parámetros también optimizados con PSO fuera de línea. Para validar la metodología propuesta, se presentan numerosas simulaciones de respuestas dinámicas del SMIB, para diferentes condiciones de operación y perturbaciones.Este trabajo ha contado con el apoyo de CONICYT-Chile, a través del proyecto FB0809 “Centro Avanzado de Tecnología para la Minería” (AMTC)”. El segundo autor agradece el apoyo de CONICYT / FONDECYT / (N ° 3140604).Duarte-Mermoud, MA.; Milla, F. (2018). Estabilizador de Sistemas de Potencia usando Control Predictivo basado en Modelo. Revista Iberoamericana de Automática e Informática industrial. 15(3):286-296. https://doi.org/10.4995/riai.2018.10056OJS286296153Abido. M.A., 2002. 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EuReCa ONE—27 Nations, ONE Europe, ONE Registry A prospective one month analysis of out-of-hospital cardiac arrest outcomes in 27 countries in Europe

AbstractIntroductionThe aim of the EuReCa ONE study was to determine the incidence, process, and outcome for out of hospital cardiac arrest (OHCA) throughout Europe.MethodsThis was an international, prospective, multi-centre one-month study. Patients who suffered an OHCA during October 2014 who were attended and/or treated by an Emergency Medical Service (EMS) were eligible for inclusion in the study. Data were extracted from national, regional or local registries.ResultsData on 10,682 confirmed OHCAs from 248 regions in 27 countries, covering an estimated population of 174 million. In 7146 (66%) cases, CPR was started by a bystander or by the EMS. The incidence of CPR attempts ranged from 19.0 to 104.0 per 100,000 population per year. 1735 had ROSC on arrival at hospital (25.2%), Overall, 662/6414 (10.3%) in all cases with CPR attempted survived for at least 30 days or to hospital discharge.ConclusionThe results of EuReCa ONE highlight that OHCA is still a major public health problem accounting for a substantial number of deaths in Europe.EuReCa ONE very clearly demonstrates marked differences in the processes for data collection and reported outcomes following OHCA all over Europe. Using these data and analyses, different countries, regions, systems, and concepts can benchmark themselves and may learn from each other to further improve survival following one of our major health care events

Hierarchical MPC Secondary Control for Electric Power System

Although in electric power systems (EPS) the regulatory level guarantees a bounded error between the reference and the corresponding system variables, to keep its availability in time, optimizing the system operation is required for operational reasons such as, economic and/or environmental. In order to do this, there are the following alternative solutions: first, replacing the regulatory system with an optimized control system or simply adding an optimized supervisory level, without modifying the regulatory level. However, due to the high cost associated with the modification of regulatory controllers, the industrial sector accepts more easily the second alternative. In addition, a hierarchical supervisory control system improves the regulatory level through a new optimal signal support, without any direct intervention in the already installed regulatory control system. This work presents a secondary frequency control scheme in an electric power system, through a hierarchical model predictive control (MPC). The regulatory level, corresponding to traditional primary and secondary control, will be maintained. An optimal additive signal is included, which is generated from a MPC algorithm, in order to optimize the behavior of the traditional secondary control system

Propuesta de interconexión a Internet

De las tres partes en las que ha sido dividido el proyecto de investigación (propuesta de Interconexión a Internet) presentamos la primera, que constituye la presentación teórica, la que permite ubicar un poco el contexto en el que se va a trabajar. Con esto pretendemos empezar a tratar de una forma cronológica todo lo necesario para llegar a entender, de manera clara, lo que es el trabajo en Internet, lo cual permitirá, posteriormente, realizar un estudio de carácter técnico

Hybrid predictive control for real-time optimization of public transport systems’ operations based on evolutionary multi-objective optimization

A hybrid predictive control formulation based on evolutionary multi-objective optimization to optimize real-time operations of public transport systems is presented. The state space model includes bus position, expected load and arrival time at stops. The system is based on discrete events, and the possible operator control actions are: holding vehicles at stations and skipping some stations. The controller (operator) pursues the minimization of a dynamic objective function to generate better operational decisions under uncertain demand at bus stops. In this work, a multi-objective approach is conducted to include different goals in the optimization process that could be opposite. In this case, the optimization was defined in terms of two objectives: waiting time minimization on one side, and impact of the strategies on the other. A genetic algorithm method is proposed to solve the multi-objective dynamic problem. From the conducted experiments considering a single bus line corridor, we found that the two objectives are opposite but with a certain degree of overlapping, in the sense that in all cases both objectives significantly improve the level of service with respect to the open-loop scenario by regularizing the headways. On average, the observed trade-off validates the proposed multi-objective methodology for the studied system, allowing dynamically finding the pseudo-optimal Pareto front and making real-time decisions based on different optimization criteria reflected in the proposed objective function compounds