Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

Li, Y.Z.; Jiang, Lai; Wang, P.; Gooi, Hoay Beng; Li, K.C.; Liu, Y.Q.; Lu, Peng; Cao, Maoyong; Imura, Jun-ichi

doi:10.1016/j.knosys.2016.10.028

Cited by 20 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Various metaheuristics such as Grey wolf optimizer (GWO) [28,29], Whale Optimization Algorithm (WOA) [30], Ant Lion Optimization (ALO) [31], Firefly Algorithm (FA) [32], Particle Swarm Optimization (PSO) [33], and Ant Colony Optimization (ACO) [34] may demonstrate superior efficiencies in tackling feature selection problems when compared to the exact methods [35,36]. Metaheuristic algorithms have shown improved results and efficiencies in dealing with many real-life applications such as path planning [37], clustering [38], and power dispatch [39]. For example, E.S.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Mafarja

Aljarah

Heidari

et al. 2018

Knowledge-Based Systems

359

149

View full text Add to dashboard Cite

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, grasshopper optimization algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and

show abstract

Section: Accepted Manuscriptmentioning

confidence: 99%

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Mafarja

Aljarah

Heidari

et al. 2018

Knowledge-Based Systems

359

149

View full text Add to dashboard Cite

show abstract

“…These strategies do not offer reasonable possibilities for dealing with the MAELD problem when prohibited operating zones (POZs), valve point loading (VPL), and multi-fuel alternatives (MFAs) are considered. To adapt to these challenges, numerous meta-heuristic methodologies have been used to take care of enhancement issues with complicated objectives [7].…”

Section: Introductionmentioning

confidence: 99%

Robust Multi-Area Economic Dispatch Using Coulomb’s and Franklin’s Laws Based Optimizer

Sakthivel¹,

Murugesan

Sathya

2020

Int. j. eng. technol. innov.

View full text Add to dashboard Cite

The multi-area economic load dispatch (MAELD) can reduce running costs through making the areas with more cost-effective units produce more energy. The excess power is transferred to the areas with expensive units. This paper contributes a new physics inspired metaheuristic approach called the Coulomb’s and Franklin’s laws based optimizer (CFLBO) to solve the MAELD problem. The CFLBO approach is developed from Coulomb’s and Franklin’s theories, which comprise attraction/repulsion, probabilistic ionization, and contact stages. The effectiveness of the envisaged CFLBO approach has been examined on three standard test systems with various areas. Results obtained by the CFLBO approach are compared with the exchange market algorithm (EMA) and the existing state-of-the-art approaches to deal with MAELD. Numerical outcomes show the benefits of the quick convergence and better quality of the suggested approach compared to existing strategies. Consequently, the proposed approach is a helpful tool for generation planning in MAELD problems.

show abstract

“…Literatürde rüzgâr-termal güç sistemlerinin EDP çözümü amacıyla şu çalışmalar bulunmaktadır: Hetzer vd. [23] tarafından rüzgâr çiftliklerinin geleneksel termal sistemlere eklenmesi ile bu sistemin matematik modelinin oluşturulması, rüzgâr enerjisi üretimi göz önünde bulundurularak rüzgâr ve termal üretim dağıtımını düzenlemek ve EDP çözümü için yeni bir doğrudan arama metodu (DAM) geliştirilmesi [24], yeni bir bulanık uyarlamalı yapay fizik optimizasyon (BU-YFO) algoritması ile rüzgâr ve termal güç jeneratörlerinin optimal güç akışının yapılması [25], rüzgâr-termik güç sistemi EDP çözümü için ortalama-alt yarı mutlak sapma (OAYMS) karar destek modeli oluşturulması ve içsel rekabet ve lévy yürüyüşü ile grup arama optimizasyon algoritmasının (İRLYGAO) uygulanması [26], Karadeniz vd. [27] tarafından rüzgâr ve termik santrallerden oluşan 6-baralı güç sisteminin ekonomik güç dağılımını büyük patlama-büyük çöküş (BP-BÇ), PSO ve iyon hareket optimizasyonu (IHO) algoritmaları ile irdelenmesi, Güçyetmez vd.…”

Section: Gi̇ri̇ş (Introduction)unclassified

Modifiye hibrit optimizasyon yöntemi ile rüzgâr-termal güç sistemleri için ekonomik dağıtım probleminin çözümü

Tefek¹,

Uğuz²

2019

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

View full text Add to dashboard Cite

Proposed a Modified Hybrid Gravitational Search Algorithm-Teaching-Learning Optimization Based Method  Searching the solution quality in the proposed MHGT method compared to the standard GSA and TLBO methods  Implementation of the proposed MHGT method to a new 19-bus Turkey wind-thermal power system for the solution of In this study, modified hybrid gravitational search algorithm (GSA)-teaching-learning based optimization (TLBO) algorithm (MHGT) method has been developed to solve the economic dispatch problem of windthermal power systems. The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. With the MHGT method, it is aimed to reach the global minimum result with the least number of iterations and to get rid of the local minimum. Figure A shows the GSA in the first search space. The second search space is based on the optimal result of the GSA algorithm. Then, TLBO run in this second search space. Figure A. Graphical representation of the MHGT method Purpose: The proposed MHGT method is aimed to solve the economic dispatch problem of 6 bus and Turkey-19 bus wind-thermal power system. Theory and Methods: In this study, firstly, equations of hybrid method were given and MHGT method was applied to benchmark test functions. Then the proposed MHGT method was applied to 6 bus and Turkey-19 bus wind-thermal power system. Results: The success of the proposed MHGT method in benchmark functions was determined by Wilcoxon signedrank test. The proposed MHGT method is wind-thermal power system of economic dispatch problem solving achievement is shown in the graphs and figures. Conclusion: It is concluded that the proposed MHGT method finds the solution in short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

show abstract

Wind-thermal power system dispatch using MLSAD model and GSOICLW algorithm

Cited by 20 publications

References 39 publications

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

Robust Multi-Area Economic Dispatch Using Coulomb’s and Franklin’s Laws Based Optimizer

Modifiye hibrit optimizasyon yöntemi ile rüzgâr-termal güç sistemleri için ekonomik dağıtım probleminin çözümü

Contact Info

Product

Resources

About