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SUMMARY This paper presents a new algorithm for harmonics estimation in power systems. Because of the nonlinearity of phases of sinusoids, the estimation of harmonic parameters is a nonlinear problem. However, nonlinear solving for amplitude estimation decreases speed of convergence. Thereby, hybrid methods decompose the harmonics estimation problem into two problems, linear for amplitude and nonlinear for phase. The objective of this paper is to introduce an accurate approach for harmonic parameters estimation. This approach is based on a stochastic search method, that is, clonal selection, to estimate the phases and a linear estimator, that is, least squares (LS), to estimate the amplitudes. This paper also indicates high accuracy of proposed algorithm in comparison with discrete Fourier transform and LS–Adaline (hybrid of LS and Adaline neural network) methods, especially in multiple frequency and highly noisy situations. Performance of the algorithm in noise rejecting even in interharmonics presence is shown by extracted simulation results of MATLAB (MathWorks Inc., Natick, MA, USA). Copyright © 2012 John Wiley & Sons, Ltd.

The paper presents the application of Monte Carlo simulation in the behavior analysis of an autonomous underwater vehicle. Due to the highly nonlinear dynamics and existence of uncertain parameters in the models, there is not a straightforward method to analyze the behavior of an autonomous underwater vehicle. The objective of this article is to introduce a Monte Carlo campaign for an autonomous underwater vehicle 6-degree-of-freedom model to examine the effects of uncertain parameters on the mission objectives. Uncertainties in the model are considered in several categories, consisting of hydrodynamic and added mass coefficients, control instruments (sensors and actuators), environmental conditions and initial conditions. Monte Carlo simulations are run for a typical autonomous underwater vehicle moving from the water surface to reach a predetermined depth and heading during the mission time. For this purpose, 6-degree-of-freedom software is developed in C++ which is a fast and visual programming software. Using an example, it is shown that simulation results can be used for tuning of guidance algorithm. Moreover, the proposed concept is applicable for analysis of other types of autonomous ocean systems.

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The main functions of the automated systems rely on the advanced sensors for detection and perception of the environment around the vehicle. Radars and cameras are commonly utilized to detect the potential obstacles and vehicles ahead on the road. Nevertheless, cameras can generate spurious detections in the extreme weather conditions such as fog, rain, dust, snow, dark, and heavy sunlight in the sky. Due to limitations in vertical field view of the radars, single radars are not reliable to detect the height of the targets precisely. In this paper, a triple radar arrangement (long-range, medium-range, and short-range radars) based on sensor fusion technique is proposed to detect objects with different size in level 2 Advanced Driver-Assistance (ADAS) system. The typical objects including truck, pedestrians, and animals are detected in different scenarios. The developed model considered ISO 26262 and ISO/PAS 21448 to reasonably address insufficient robustness and inability of the sensors. The models of sensor and level 2 ADAS systems are developed using MATLAB toolbox and Simulink. Sensor detection performance is determined by running simulations with triple radar setup. Obtained results demonstrate that the proposed approach generates accurate detections of targets in all tested scenarios.