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M. Nezhadshahbodaghi, K. Bahmani, M. R. Mosavi, D. Martín,
Volume 19, Issue 2 (June 2023)
Abstract

Today, it can be said that in every field in which timely information is needed, we can use the applications of time-series prediction. In this paper, among so many chaotic systems, the Mackey-Glass and Loranz are chosen. To predict them, Multi-Layer Perceptron Neural Network (MLP NN) trained by a variety of heuristic methods are utilized such as genetic, particle swarm, ant colony, evolutionary strategy algorithms, and population-based incremental learning. Also, in addition to expressed methods, we propose two algorithms of Bio-geography-Based Optimization (BBO) and fuzzy system to predict these chaotic systems. Simulation results show that if the MLP NN is trained based on the proposed meta-heuristic algorithm of BBO, training and testing accuracy will be improved by 28.5% and 51%, respectively. Also, if the presented fuzzy system is utilized to predict the chaotic systems, it outperforms approximately by 98.5% and 91.3% in training and testing accuracy, respectively.

 

M. K. Rashid, A. M. Mohammed,
Volume 19, Issue 2 (June 2023)
Abstract

Nowadays, magnetic gears (MGs) have become an alternative choice for mechanical gears because of their low maintenance, improved durability, indirect contact between inner and outer rotors, no lubrication, and high efficiency. Generally, although these advantages, MGs suffer from inherent issues, mainly the cogging torque. Therefore, cogging torque mitigation has become an active research area. This paper proposed a new cogging torque mitigation approach based on the radial slit of the ferromagnetic pole pieces of MGs. In this method, different numbers and positions of slits are applied. The best results are gained through an even number of slits which shows promising results of cogging torque mitigation on the inner rotor with a small mitigation in the mean torque on both rotors. This work is done by using Simcenter and MATLAB software packages. The inner rotor’s cogging torque has mitigated to 81.9 %, while the outer rotor’s cogging torque is increased only by 2.75 %.

Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (December 2023)
Abstract

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.


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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.