Programmable logic design of a compact Genetic Algorithm for phasor estimation in real-time

The main objective of this work is to present an efficient method for phasor estimation based on a compact Genetic Algorithm (cGA) implemented in Field Programmable Gate Array (FPGA). To validate the proposed method, an Electrical Power System (EPS) simulated by the Alternative Transients Program (ATP) provides data to be used by the cGA. This data is as close as possible to the actual data provided by the EPS. Real life situations such as islanding, sudden load increase and permanent faults were considered. The implementation aims to take advantage of the inherent parallelism in Genetic Algorithms in a compact and optimized way, making them an attractive option for practical applications in real-time estimations concerning Phasor Measurement Units (PMUs).

A control and automation system for wave basins

This paper presents the new active absorption wave basin, named Hydrodynamic Calibrator (HC), constructed at the University of São Paulo (USP), in the Laboratory facilities of the Numerical Offshore Tank (TPN). The square (14 m 14 m) tank is able to generate and absorb waves from 0.5 Hz to 2.0 Hz, by means of 148 active hinged flap wave makers. An independent mechanical system drives each flap by means of a 1HP servo-motor and a ball-screw based transmission system. A customized ultrasonic wave probe is installed in each flap, and is responsible for measuring wave elevation in the flap. A complex automation architecture was implemented, with three Programmable Logic Computers (PLCs), and a low-level software is responsible for all the interlocks and maintenance functions of the tank. Furthermore, all the control algorithms for the generation and absorption are implemented using higher level software (MATLAB /Simulink block diagrams). These algorithms calculate the motions of the wave makers both to generate and absorb the required wave field by taking into account the layout of the flaps and the limits of wave generation. The experimental transfer function that relates the flap amplitude to the wave elevation amplitude is used for the calculation of the motion of each flap. This paper describes the main features of the tank, followed by a detailed presentation of the whole automation system. It includes the measuring devices, signal conditioning, PLC and network architecture, real-time and synchronizing software and motor control loop. Finally, a validation of the whole automation system is presented, by means of the experimental analysis of the transfer function of the waves generated and the calculation of all the delays introduced by the automation system.

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques. (C) 2012 Elsevier Inc. All rights reserved.