Truncated and Spheroidal Ag Nanoparticles: A Matter of Size Transformation

The ordered arrays of anisotropic mesostructure metal nanoparticle (diameter size in the range of 15 to 200 nm) characteristics are indeed influenced by the combined effect of packing constraints and inter-particle interactions, that is, the two morphological factors that strongly influence the creation of the particles’ shape. In this work, we studied on how the degree of truncation of Ag nanoparticles authorised the mesostructured morphologies and particle orientation preferences within the mesosparticle arrays. The Ag represented the best and most versatile candidate and known for its highest electrical conductivities among other transition metals in periodic table. The interest is motivated by the need to understand the inevitable morphological transformation from mesoscopic to microscopic states evolve within the scope of progressive aggregation of atomic constituents of Ag system. The grazing information obtained from HR-TEM shows that Ag mesosparticles of highly truncated flake are assembled in fcc-type mesostructure, similar to the arrays formed by microscopic quasi-spherical structure, but with significantly reduced packing density and different growth orientations. The detailed information on the size and microstructure transformation have been gathered by fast Fourier transform (FFT) of HR-TEM images, allowing us to figure out the role of Ag defects that anchored the variation in crystallite growth of different mean diameter size particles. The influences on the details of the nanostructures have to be deeply understood to promote practical applications for such outstanding Ag material

Kalman Filter Estimation of Impedance Parameters for Medium Transmission Line

Accurate knowledge of impedance parameters in transmission line helps to improve the system efficiency and performance. Nowadays, the estimation of impedance parameters in transmission line has become possible with the availability of computational method. This paper aims to develop Kalman filter model by using Matlab simulink to estimate accurate values of resistance (R), reactance (X), and susceptance (B) in medium transmission line. The accuracy of the parameters can be improved by reducing the unknown errors in the system. To demonstrate the effectiveness of the Kalman filter method, a case study of simulated medium transmission line is presented and comparison between Kalman Filter (KF) and Linear Least Square (LLS) method is also considered to evaluate their performances

A simulation-metaheuristic approach for finding the optimal allocation of the battery energy storage system problem in distribution networks

This paper proposes a simulation study to solve the optimal allocation of the Battery Energy Storage System (BESS) problem in distribution networks. The effect of BESS's installation in the selected distribution networks is surveyed for a 24-hour period, where time-of-use electricity charges are divided into three periods: standard, peak, and off-peak hours. This study will use Teaching Learning-Based Optimization (TLBO) as the main optimizer for the problem simulation. The objective function is to minimize the combined cost of purchasing electricity and energy loss, where the optimal location of BESS and its operated power at each hour are treated as the control variables to be optimized. Two distribution systems are utilized, viz. 18-node and 33-node systems are considered to assess the performance of TLBO in solving the mentioned problem, where a comparison with other recent metaheuristic algorithms also have been conducted. The study's findings demonstrated the promising results of TLBO in terms of minimizing the energy cost and significantly reducing the peak loads during peak hours in the 24 h. The simulations also show that TLBO can be used as an effective tool for position and power of BESS optimization solution, where for the 18-node system, there is about 3.7 % cost reduction and for the 33-node system, about 12% cost saving for power purchased for the surveyed 24-h period

Data Association Analysis In Simultaneous Localization And Mapping Problem

This paper examines the data association issues in Simultaneous Localization and Mapping Problem on two different techniques. Data association determines the system  efficiency and there are limited numbers of papers attempts to analyze the conditions. Two filters namely the Extended Kalman Filter(EKF) and H∞ Filters are considered in this paper to improved the estimation results of both mobile robot and the environment locations. The updated state covariance is modified to obtain better performance compared to its original state. The simulation results have shown consistency and lower percentage of errors for the proposed technique. However, there are certain cases that showing the updated state covariance becomes unstable and yields erroneous results especially for EKF. Hence, further works are expected to be carried for this matter

Improved barnacles mating optimizer for loss minimization problem in optimal reactive power dispatch

The solution of Optimal Reactive Power Dispatch (ORPD) can be treated as one of the sub-Optimal Power Flow (OPF) problems where the loss minimization is one of the objective functions to be solved. In this paper, an improvement of recent algorithm namely Improved Barnacles Mating optimizer (IBMO) is proposed to determine the best combination of control variables of power system's components such as generator bus voltages, injected MVAR devices and transformer ratios so that the total transmission loss can be minimized. To assess the performance of IBMO in loss minimization of ORPD, IEEE 57-bus system will be used. The performance of IBMO will be compared with original BMO and Moth-Flame optimizer (MFO) to show the effectiveness of proposed improvement in solving the ORPD problem

Using the barnacles mating optimizer with effective constraints handling technique for cost minimization of optimal power flow solution

Optimal Power Flow (OPF) solution is one of the active research topics in power system optimization problems. It is one of the complex non-linear optimization problems where the determination of economical and efficient operation should be done by obtaining the steady state of electrical components in power networks. Various metaheuristic algorithms have been utilized in the last decades to solve OPF. However, the constraints of OPF are normally solved by implementing the penalty function approach which require tedious trial and error to obtain the penalty function’s selection. This paper proposes the constraint handling technique namely superiority of feasible solution (SF) that integrated with the recent metaheuristic algorithm, viz. Barnacles Mating Optimize (BMO) to be implemented of OPF problem, specifically in cost minimization. The approach is tested on IEEE 30-bus system and compared with the other metaheuristic algorithm with SF approach too. From the comparison, it can be concluded that the performance of SF-BMO is better compared to others in terms of obtaining the minimum cost of power generation

Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle

This paper presents the application of a recent metaheuristic algorithm namely Evolutionary Mating Algorithm (EMA) for optimizing the Deep Learning (DL) parameters to estimate the state of charge (SOC) of a battery for an electric vehicle in the real environment. The recorded data were obtained from 70 real driving trips of a BMW i3 EV, where the inputs of the DL were the voltage, current, battery temperature and ambient temperature while the output was the real SOC recorded during all trips. The data were divided into 60 trips for training and the final 10 trips for testing the performance of the developed EMA-DL model. The findings of the study demonstrated the promising results of EMA-DL in terms of obtaining the minimum error, which significantly increases the accuracy of the SOC estimation. To show the effectiveness of EMA-DL, comparison studies were conducted among other metaheuristic optimizers that were also used to optimize the DL parameters viz, Particles Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) as well as the Adaptive Moment Estimation (ADAM). According to the simulation results, the proposed EMA-DL algorithm was found to outperform all the other compared algorithms based on the evaluated metrics. Thus, it can be employed as a proficient technique to accurately estimate the state of charge (SOC) of electric vehicle batteries

A sensitive ac magnetometer using a resonant excitation coil for characterization of magnetic fluid in nonlinear magnetization region

In order to tailor magnetic nanoparticles (MNPs) for intended applications, it is important to unravel their dynamics with respect to excitation magnetic field. In this work, we report on the development of a sensitive AC magnetometer using a resonant excitation coil for this purpose. The excitation coil fabricated from a Litz wire is connected to a capacitor network to effectively reduce the impedance of the circuit. The high efficiency showed by the excitation coil enables investigation of MNP’s dynamics in the nonlinear magnetization region. We demonstrate the sensitivity of the developed system by measuring the harmonics of a multicore iron oxide nanoparticle solution down to 300 ng/ml of iron concentration. We experimentally show that the first harmonic component is not completely ‘transparent’ to the diamagnetic background of the carrier liquid compared to the higher harmonics. We also demonstrate the complex magnetization measurement of the iron oxide nanoparticles in solution and dry states from 3 Hz to 18 kHz. A highly sensitive exploration of MNPs’ dynamics can be expected using the developed AC magnetometer

Evolutionary mating algorithm

This paper proposes a new evolutionary algorithm namely Evolutionary Mating Algorithm (EMA) to solve constrained optimization problems. The algorithm is based on the adoption of random mating concept from Hardy–Weinberg equilibrium and crossover index in order to produce new offspring. In this algorithm, effect of the environmental factor (i.e. the presence of predator) has also been considered and treated as an exploratory mechanism. The EMA is initially tested on the 23 benchmark functions to analyze its effectiveness in finding optimal solutions for different search spaces. It is then applied to Optimal Power Flow (OPF) problems with the incorporation of Flexible AC Transmission Systems (FACTS) devices and stochastic wind power generation. The extensive comparative studies with other algorithms demonstrate that EMA provides better results and can be used in solving real optimization problems from various fields