A comparative analysis using different intelligent capable of solving efficiently optimization problems with a techniques has been carried out for the Economic Load non-convex, non-continuous, and highly nonlinear solution Dispatch (ELD) problem considering line flow constraints for space [7][8]. the regulated power system to ensure a practical, economical Recent advances in computation, and the search for better and secure generation schedule. The objective of this paper is results of complex optimization problems, have fomented to minimize the total production cost of the thermal power the development of a group of techniques known as generation. Economic Load Dispatch (ELD) has been applied Evolutionary Algorithms [9][10][11][12][13][14][15]. Evolutionary Algorithms to obtain optimal fuel cost. Optimal Power Flow has been carried out to obtain ELD solutions with minimum operating are stochastlc based optmzation techniques that search for cost satisfying both unit and network constraints. In this the solution of problems using a simplified model of the paper, various intelligent techniques such as Genetic evolutionary process found in nature. Algorithm (GA), Evolutionary Programming (EP), Particle These algorithms provide an alternative for obtaining Swarm Optimization (PSO), and Differential Evolution (DE) global optimal solutions, especially in the presence of nonhave been applied to obtain ELD solutions. The proposed continuous, non-convex, and highly nonlinear solution algorithm has been tested on two sample systems viz IEEE 30 spaces. The success of evolutionary algorithms is partly bus system and a 15 unit system. The results obtained by the due to their inherent capability of processing a population various intelligent techniques are compared. The solutions obtained are quite encouraging and useful in the economic o p environment. The algorithm and simulation are carried out perform an extensive exploration of the search space.using Matlab software.Besides, they do not rely on derivative information to guide the search toward the problem solution. Instead, they use
Peer-to-Peer (P2P) electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer. It also decreases the quantity of line loss incurred in Smart Grid (SG). But, uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer. In recent times, numerous Machine Learning (ML)-enabled load predictive techniques have been developed, while most of the existing studies did not consider its implicit features, optimal parameter selection, and prediction stability. In order to overcome fulfill this research gap, the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm (MOGOA) with Deep Extreme Learning Machine (DELM)-based short-term load predictive technique i.e., MOGOA-DELM model for P2P Energy Trading (ET) in SGs. The proposed MOGOA-DELM model involves four distinct stages of operations namely, data cleaning, Feature Selection (FS), prediction, and parameter optimization. In addition, MOGOA-based FS technique is utilized in the selection of optimum subset of features. Besides, DELM-based predictive model is also applied in forecasting the load requirements. The proposed MOGOA model is also applied in FS and the selection of optimal DELM parameters to improve the predictive outcome. To inspect the effectual outcome of the proposed MOGOA-DELM model, a series of simulations was performed using UK Smart Meter dataset. In the experimentation procedure, the proposed
Multilevel inverters have gained much attention for its operation involving applications ranging values of high power rating. This paper proposes a switching topology for asymmetric multilevel inverter utilizing less number of power electronics components. When the number of the output level increases, it requires more switching states and eventually the number of switching components. The increased number of switches results in higher switching losses which may lead to power loss, and reduction of efficiency of the overall conversion system. The salient feature of this proposed topology is that the module can be used as a sub multiple level structure and can be extended for any number of level with minimal increase in the switching components.
The usage of various inverters in various industries has gained considerable attention in the power electronics industry in recent years. The harmonic distortion that is induced by various renewable energy sources, along with the growing use of nonlinear loads and power electronic devices, has a significant impact on the distribution system. This is because of the rise in penetration of various renewable energy sources and the usage of nonlinear loads and power electronic devices. In addition, distributed generation (DG) units with inverters (such as solar (PV) and wind turbines) that are integrated into distribution networks are viewed as significant harmonic producers that have substantial adverse effects on power quality. The power quality in the system suffers because of these generators. This study proposes a method of harmonic mitigation for addressing power quality problems that are present in distribution systems as a solution to the challenges that have been found. These concerns have been brought to light as a result of previous research. The standard two-level and three-level inverters were the basis for the development of the multilevel inverter (MLI), which was meant to address their shortcomings. One of the effective technologies that can maintain constant performance is the hybrid power generating system. A hybrid energy system has several benefits, including high dependability, cheap cost and minimal emissions. When used in a hybrid power production system, the reduction of total harmonic distortion (THD) becomes absolutely necessary (HPGS). In this context, the research provides a novel approach for high-performance quantum computing called quantum coyote optimization algorithm-based THD reduction (QCOA-THDR). The QCOA-THDR approach that has been presented has been tested with several converters, including SEPIC, buck–boost and Cuk converters. Additionally, the proportional as well as the integral gain variables of the proportional integral (PI) controller are set in order to achieve decreased total harmonic distortion (THD). In addition, the QCOA system is formed by combining the ideas of quantum computing (QC) with the traditional COA. This is how the QCOA system comes to exist. A limited number of simulations were run, and the results are being analyzed from a variety of perspectives in order to investigate the improved effects that the QCOA-THDR approach has on data. The QCOA-THDR method was shown to have superior results than the more modern techniques, as shown by the comparison research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.