scholarly journals Estabilizador de Sistemas de Potencia usando Control Predictivo basado en Modelo

2018 ◽  
Vol 15 (3) ◽  
pp. 286 ◽  
Author(s):  
Manuel A Duarte-Mermoud ◽  
Freddy Milla
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Particle Swarm ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Predictive Controller ◽  
Single Machine Infinite Bus

<p>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.</p>

scholarly journals Aplicación del control inteligente en oscilaciones usando FACTS (STATCOM y SVC)

Tecnura ◽  
2017 ◽  
Vol 21 (53) ◽  
pp. 47-60
Author(s):  
Alfonso James Alzate Gomez ◽  
Andrea Bedoya Londoño ◽  
Jesser James Marulanda Durango
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Single Machine ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Static Var Compensator ◽  
Static Synchronous Compensator ◽  
Swarm Optimization ◽  
Single Machine Infinite Bus

Contexto: Reducir las oscilaciones de un sistema eléctrico de potencia es necesario para mantener la estabilidad del mismo. En este trabajo se presenta una metodología para la sintonización de un controlador de oscilaciones de tipo difuso, un compensador de Volts Ampere Reactive, (VAR, por sus siglas en inglés), uno estático (Static Var Compensator, SVC, por sus siglas en inglés), y un compensador estático síncrono (Static Synchronous Compensator, STATCOM, por sus siglas en inglés), por métodos inteligentes.Método: Consiste en sintonizar un controlador de tipo difuso para amortiguar las oscilaciones de un sistema eléctrico de potencia por medio de un STATCOM o un SVC, a través de diferentes técnicas de optimización tales como: algoritmos genéticos (GA, Genetic Algorithm), optimización por enjambre de partículas (PSO, Particle Swarm Optimization) y algoritmo de optimización caótica (COA, Chaotic Optimization Algorithm).Resultados: A través de simulación se obtienen las oscilaciones en el voltaje y la velocidad de un sistema compuesto por una máquina sincróna conectada a un barraje infinito (SMIB, Single Machine Infinite Bus) antes y después de conectar un SVC y un STATCOM, ambos instalados independientemente y en diferentes condiciones de operación. Los resultados muestran las ventajas de utilizar métodos de ajuste para el controlador difuso comparados con el ajuste a ensayo y error.Conclusión: A partir de los resultados obtenidos, se comprueba la efectividad del controlador difuso utilizado en el control de oscilaciones con dispositivos Flexible AC Transmissions Systems (FACTS, por sus siglas en inglés) tanto en el SVC como en el STATCOM.

scholarly journals An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Kun Li ◽  
Huixin Tian
Keyword(s):  
Particle Swarm Optimization ◽  
Single Machine ◽  
Particle Swarm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Setup Costs ◽  
Sequence Dependent Setup ◽  
Sequence Dependent Setup Costs

This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO) is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints

Kybernetes ◽  
2014 ◽  
Vol 43 (9/10) ◽  
pp. 1469-1482 ◽  
Author(s):  
Adel Taeib ◽  
Moêz Soltani ◽  
Abdelkader Chaari
Keyword(s):  
Particle Swarm Optimization ◽  
Predictive Control ◽  
Fuzzy Controller ◽  
Nonlinear Process ◽  
Model Predictive Controller ◽  
Swarm Optimization ◽  
Content Type ◽  
Control Scheme ◽  
Predictive Controller ◽  
New Type

Purpose – The purpose of this paper is to propose a new type of predictive fuzzy controller. The desired nonlinear system behavior is described by a set of Takagi-Sugeno (T-S) model. However, due to the complexity of the real processes, obtaining a high quality control with a short settle time, a periodical step response and zero steady-state error is often a difficult task. Indeed, conventional model predictive control (MPC) attempts to minimize a quadratic cost over an extended control horizon. Then, the MPC is insufficient to adapt to changes in system dynamics which have characteristics of complex constraints. In addition, it is shown that the clustering algorithm is sensitive to random initialization and may affect the quality of obtaining predictive fuzzy controller. In order to overcome these problems, chaos particle swarm optimization (CPSO) is used to perform model predictive controller for nonlinear process with constraints. The practicality and effectiveness of the identification and control scheme is demonstrated by simulation results involving simulations of a continuous stirred-tank reactor. Design/methodology/approach – A new type of predictive fuzzy controller. The proposed algorithm based on CPSO is used to perform model predictive controller for nonlinear process with constraints. Findings – The results obtained using this the approach were comparable with other modeling approaches reported in the literature. The proposed control scheme has been show favorable results either in the absence or in the presence of disturbance compared with the other techniques. It confirms the usefulness and robustness of the proposed controller. Originality/value – This paper presents an intelligent model predictive controller MPC based on CPSO (MPC-CPSO) for T-S fuzzy modeling with constraints.

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