F.2.11. Applications
Ali Sedehi; Alireza Alfi; Mohammadreza Mirjafari
Abstract
This paper addresses a key challenge in designing a suitable controller for DC-DC converters to regulate the output voltage effectively within a limited time frame. In addition to non-minimum phase behavior of such type of converter, a significant issue, namely parametric uncertainty, can further complicate ...
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This paper addresses a key challenge in designing a suitable controller for DC-DC converters to regulate the output voltage effectively within a limited time frame. In addition to non-minimum phase behavior of such type of converter, a significant issue, namely parametric uncertainty, can further complicate this task. Robust control theory is an efficient approach to deal with this problem. However, its implementation often requires high-order controllers, which may not be practical due to hardware and computational constraints. Here, we propose a low-order robust controller satisfying the robust stability and performance criteria of conventional high-order controllers. To tackle this issue, a constraint optimization problem is formulated, and the evolutionary algorithms are adopted to achieve the optimal parameter values of the controller. Both simulation and experimental outcomes have been documented, and a comparative analysis with an optimal Proportional-Integral (PI) controller has been conducted to substantiate efficiency to the proposed methodology.
H.3. Artificial Intelligence
Saheb Ghanbari Motlagh; Fateme Razi Astaraei; Mojtaba Hajihosseini; Saeed Madani
Abstract
This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to ...
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This study explores the potential use of Machine Learning (ML) techniques to enhance three types of nano-based solar cells. Perovskites of methylammonium-free formamidinium (FA) and mixed cation-based cells exhibit a boosted efficiency when employing ML techniques. Moreover, ML methods are utilized to identify optimal donor complexes, high blind temperature materials, and to advance the thermodynamic stability of perovskites. Another significant application of ML in dye-sensitized solar cells (DSSCs) is the detection of novel dyes, solvents, and molecules for improving the efficiency and performance of solar cells. Some of these materials have increased cell efficiency, short-circuit current, and light absorption by more than 20%. ML algorithms to fine-tune network and plasmonic field bandwidths improve the efficiency and light absorption of surface plasmonic resonance (SPR) solar cells. This study outlines the potential of ML techniques to optimize and improve the development of nano-based solar cells, leading to promising results for the field of solar energy generation and supporting the demand for sustainable and dependable energy.
F.2.7. Optimization
Mahsa Dehbozorgi; Pirooz Shamsinejadbabaki; Elmira Ashoormahani
Abstract
Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual ...
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Clustering is one of the most effective techniques for reducing energy consumption in wireless sensor networks. But selecting optimum cluster heads (CH) as relay nodes has remained as a very challenging task in clustering. All current state of the art methods in this era only focus on the individual characteristics of nodes like energy level and distance to the Base Station (BS). But when a CH dies it is necessary to find another CH for cluster and usually its neighbor will be selected. Despite existing methods, in this paper we proposed a method that considers node neighborhood fitness as a selection factor in addition to other typical factors. A Particle Swarm Optimization algorithm has been designed to find best CHs based on intra-cluster distance, distance of CHs to the BS, residual energy and neighborhood fitness. The proposed method compared with LEACH and PSO-ECHS algorithms and experimental results have shown that our proposed method succeeded to postpone death of first node by 5.79%, death of 30% of nodes by 25.50% and death of 70% of nodes by 58.67% compared to PSO-ECHS algorithm
H.3.2.7. Industrial automation
M. Aghaei; A. Dastfan
Abstract
The harmonic in distribution systems becomes an important problem due to an increase in nonlinear loads. This paper presents a new approach based on a graph algorithm for optimum placement of passive harmonic filters in a multi-bus system, which suffers from harmonic current sources. The objective of ...
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The harmonic in distribution systems becomes an important problem due to an increase in nonlinear loads. This paper presents a new approach based on a graph algorithm for optimum placement of passive harmonic filters in a multi-bus system, which suffers from harmonic current sources. The objective of this paper is to minimize the network loss, the cost of the filter and the total harmonic distortion of voltage, and also enhances voltage profile at each bus effectively. Four types of sub-graph have been used for search space of optimization. The method handles standard capacitor sizes in planning filters and associated costs. In this paper, objective function is not differential but eases solving process. The IEEE 30 bus test system is used for the placement of passive filter. The simulation has been done to show applicability of the proposed method. Simulation results prove that the method is effective and suitable for the passive filter planning in a power system.
Morteza Haydari; Mahdi Banejad; Amin Hahizadeh
Abstract
Restructuring the recent developments in the power system and problems arising from construction as well as the maintenance of large power plants lead to increase in using the Distributed Generation (DG) resources. DG units due to its specifications, technology and location network connectivity can improve ...
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Restructuring the recent developments in the power system and problems arising from construction as well as the maintenance of large power plants lead to increase in using the Distributed Generation (DG) resources. DG units due to its specifications, technology and location network connectivity can improve system and load point reliability indices. In this paper, the allocation and sizing of distributed generators in distribution electricity networks are determined through using an optimization method. The objective function of the proposed method is based on improving the reliability indices, such as a System Average Interruption Duration Index (SAIDI), and Average Energy Not Supplied (AENS) per customer index at the lowest cost. The optimization is based on the Modified Shuffled Frog Leaping Algorithm (MSFLA) aiming at determining the optimal DG allocation and sizing in the distribution network. The MSFLA is a new mimetic meta-heuristic algorithm with efficient mathematical function and global search capability. To evaluate the proposed algorithm, the 34-bus IEEE test system is used. In addition, the finding of comparative studies indicates the better capability of the proposed method compared with the genetic algorithm in finding the optimal sizing and location of DG’s with respect to the used objective function.
