Crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system

2018 ◽  
Author(s):  
Sudeep Kumar ◽  
Akshay Kumar ◽  
Gauri Shankar
Keyword(s):  
Search Algorithm ◽  
Dynamic Performance ◽  
Performance Control ◽  
Optimal Dynamic

An Efficient Control Implementation for Inverter Based Harmony Search Algorithm

2017 ◽  
Vol 8 (1) ◽  
pp. 279
Author(s):  
Mushtaq Najeeb ◽  
Hamdan Daniyal ◽  
Ramdan Razali ◽  
Muhamad Mansor
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Dynamic Performance ◽  
Harmony Search ◽  
Absolute Error ◽  
Fast Response ◽  
Pi Controller ◽  
Harmony Search Algorithm ◽  
Voltage Source ◽  
Output Performance

This research implements a PI controller based on harmony search (HS) optimization algorithm for voltage source inverter to improve the output performance under step load change conditions. The HS algorithm aims to handle the trial and error procedure used in finding the PI parameters and then apply the proposed control algorithm via the eZdsp TMS320F28355 board to link the inverter prototype with the Matlab Simulink. The mean absolute error (MAE) is used as an optimization problem to minimize the output voltage error for the developed controller (PI-HS) as compared to the PI controller based particale swarm optimization algorithm (PI-PSO). Based on the experimental results obtained, it is noted that the proposed controller (PI-HS) provides a good dynamic performance, robustness, constant voltage amplitude, and fast response in terms of overshoot, transient, and steady-state.

scholarly journals Optimal dynamic empirical therapy in a health care facility: an artificial intelligence approach

2019 ◽  
Author(s):  
Nicolas Houy ◽  
Julien Flaig
Keyword(s):  
Health Care ◽  
Infected Patient ◽  
Search Algorithm ◽  
Care Facility ◽  
Health Care Facility ◽  
Empirical Therapy ◽  
Population Level ◽  
Monte Carlo Tree Search ◽  
Stochastic Version ◽  
Optimal Dynamic

AbstractWe propose a solution to the problem of finding an empirical therapy policy in a health care facility that minimizes the cumulative infected patient-days over a given time horizon. We assume that the parameters of the model are known and that when the policy is implemented, all patients receive the same treatment at a given time. We model the emergence and spread of antimicrobial resistance at the population level with the stochastic version of a compartmental model. The model features two drugs and the possibility of double resistance. Our solution method is a variant of the Monte-Carlo tree search algorithm. In our example, this method allows to reduce the cumulative infected patient-days over two years by 22% compared to the best standard therapy.

Router Power Reduction through Dynamic Performance Control Based on Traffic Predictions

2012 ◽  
Vol E95.B (10) ◽  
pp. 3130-3138 ◽  
Author(s):  
Hiroyuki ITO ◽  
Hiroshi HASEGAWA ◽  
Ken-ichi SATO
Keyword(s):  
Dynamic Performance ◽  
Power Reduction ◽  
Performance Control

Fuzzy Logic Based Dynamic Performance Investigation of Moisture Control in Paper Industry

2014 ◽  
Vol 573 ◽  
pp. 254-259
Author(s):  
M. Senthil Kumar ◽  
K. Mahadevan
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Performance ◽  
Paper Industry ◽  
Conventional Technique ◽  
Computing Technique ◽  
Pid Tuning ◽  
Performance Specification ◽  
Optimal Dynamic ◽  
Tuning Methods ◽  
Fuzzy Logic Technique

Proportional – Integral – Derivatives control scheme is used to provide an efficient and quiet easier in control engineering applications. Most of the Conventional PID tuning methods are used in manually which is difficult and time consuming. Soft Computing Technique is used to overcome this problem by using Fuzzy logic technique. In this research focuses dynamic performance specification is such as rise time, settling time and peak over shoot. This paper proposes to compare conventional and non-conventional techniques and the result of this comparison, The non-conventional technique (Fuzzy logic) has optimal dynamic performance. The plant model is represented by the transfer function, is obtained by the system identification tool box.

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