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optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm

ENERGY 2023年第1期285卷

作者： Toopshekan, Ashkan Abedian, Ali Azizi, Arian Ahmadi, Esmaeil Rad, Mohammad Amin Vaziri Univ Tehran Fac New Sci & Technol Dept Renewable Energies Tehran Iran Shahid Beheshti Univ Dept Elect Engn Tehran Iran Univ Texas Dallas Dept Elect & Comp Engn Richardson TX USA Kyoto Univ Grad Sch Energy Sci Kyoto Japan

Using optimization algorithms and developing dispatch strategies are essential in sizing renewable energy systems to ensure optimal performance, cost-effectiveness, and sustainability. This study employs the teaching learning-based optimization (TLBO) algorithm to determine the optimal size of a Combined Heat and Power (CHP) system. The optimization results are validated using the Genetic Algorithm (GA) and Particle Swarm optimization (PSO). Furthermore, a novel dispatch strategy is developed to make an informed decision when using different energy sources. The strategy considers a 24-h foresight of upcoming electrical demand, solar irradiation, temperature, and wind speed. The developed dispatch strategy has led to a reduction in cost and excess electricity compared to the pre-prepared strategies. The energy sources employed include Photovoltaic panels (PV), Wind Turbines (WT), Diesel Generators (DG) with heat recovery capability, battery banks, and boilers to supply electrical and thermal demand. A Levelized cost of energy (LCOE) of 0.142 $/kWh is obtained for the PV/WT/DG/Battery/Boiler system. Although the three algorithms find almost similar optimal solutions, TLBO exhibits better convergence speed than PSO and GA. A comparison with HOMER software control strategies shows the developed dispatch strategy is 3.4% and 15.5% more efficient than Cycle Charging and Load Following strategies, respectively. Lastly, a comprehensive economic sensitivity analysis is performed to investigate the effect of inflation and discount rates on the size of components and final objective functions.

关键词： Renewable energy Forecasting dispatch teaching learning based optimization Combined heat and power

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A new chaotic teaching learning based optimization for frequency reconfigurable antennas design

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第2期36卷 1353-1360页

作者： Zadehparizi, Fatemeh Jam, Shahrokh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper, a new chaotic teaching learning based optimization (CTLBO) is proposed. TLBO is a rather newly proposed population-based algorithm. This algorithm has no control parameters for the tuning and has a simple structure. We improve its performance by chaotic maps. First, the presented CTLBO is tested on nine unimodal/multimodal benchmark functions. Then, chaotic sequences are applied as vectors with different initial values for design of a frequency reconfigurable antenna (FRA) as a practical example. Comparisons of the performance of this algorithm with those of the basic TLBO, genetic algorithm and particle swarm optimization show the ability of this algorithm in design of FRAs in terms of faster convergence and better performance. A prototype of the optimized antenna with CTLBO algorithm is fabricated and the simulation and measurement results agree suitably.

关键词： teaching learning based optimization Chaos theory Frequency reconfigurable antenna

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Forgery Detection of Digital Images Using teaching-learning based optimization and Principal Component Analysis

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SENSING AND IMAGING 2023年第1期24卷 19-19页

作者： Uma, S. Sathya, P. D. Annamalai Univ Dept Elect & Commun Engn Chidambaram 608002 Tamil Nadu India

Present day image processing tools enable the user to modify the contents of digital images for improving the quality, colour balance, appearance, and so on. They can also be used for creating false evidence for insurance claim, law of court, medical diagnosis and treatment, thereby requiring authentication before using digital images as evidences. The interest point based forgery detection techniques are well-accepted as they are computationally better than block based approaches, still they fail to detect when the forged region is smooth with low gradients. This paper endeavours to use a limited number of interest points, detected by "Difference of Gaussian" and "Features from Accelerated Segment Test" techniques, and employ "Scale Invariant Features Transform" descriptors, besides applying "Principal Component Analysis" for feature reduction and "Random Sample Consensus" for elimination of false matches. It also employs "teaching-Leaning based optimization" for clustering the features with a view of obtaining the global best clusters. It studies the performances on 500 digital images and portrays the performances of the proposed method.

关键词： Forgery detection teaching learning based optimization SIFT RANSAC

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Socio-Inspired Multi-Cohort Intelligence and teaching-learning-based optimization for Hydraulic Fracturing Parameters Design in Tight Formations

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JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME 2022年第7期144卷 073201-073201页

作者： Muther, Temoor Syed, Fahad Iqbal Dahaghi, Amirmasoud Kalantari Negahban, Shahin Univ Kansas Dept Chem & Petr Engn Lawrence KS 66045 USA

Hydraulic fracturing is one of the revolutionary technologies widely applied to develop tight hydrocarbon reservoirs. Moreover, hydraulic fracture design optimization is an essential step to optimize production from tight reservoirs. This study presents the implementation of three new socio-inspired algorithms on hydraulic fracturing optimization. The work integrates reservoir simulation, artificial neural networks, and preceding optimization algorithms to attain the optimized fractures. For this study, a tight gas production dataset is initially generated numerically for a defined set of the fracture half-length, fracture height, fracture width, fracture conductivity, and the number of fractures' values. Secondly, the generated dataset is trained through a neural network to predict the effects of preceding parameters on gas production. Lastly, three new socio-inspired algorithms including cohort intelligence (CI), multi-cohort intelligence (multi-CI), and teaching learning-based optimization (TLBO) are applied to the regressor output to obtain optimized gas production performance with the combination of optimum fracture design parameters. The results are then compared with the traditionally used optimizers including particle swarm optimization (PSO) and genetic algorithm (GA). The results demonstrated that the multi-CI and TLBO converge at the global best position more often with a success rate of at least 95% as compared to CI, PSO, and GA. Moreover, the CI, PSO, and GA are found to stuck many times at the local maximum. This concludes that the multi-CI and TLBO are good alternatives to PSO and GA considering their high performance in determining the optimum fracture design parameters in comparison.

关键词： fracture half-length fracture conductivity hydraulic fracture optimization particle swarm optimization genetic algorithm cohort intelligence teaching learning based optimization hydrates coal bed methane heavy oil oil sands tight gas oil gas reservoirs petroleum engineering petroleum wells-drilling production construction unconventional petroleum

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Binary Classification of Static Security Assessment using teaching learning based optimization enhanced Support Vector Machine 16

Binary Classification of Static Security Assessment using Te...

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16th IEEE-India-Council International Conference (INDICON)

作者： Dhandhia, Astik Pandya, Vivek Pandit Deendayal Petr Univ Elect Engn Dept Gandhinagar Gujarat India

ISBN: (纸本)9781728123271

Static security assessment is very essential for secure and reliable operation of power system. Lengthy calculation time and large memory requirement are the main drawbacks of the conventional power flow methods. The composite security index has been implemented to prevent the problem of masking associated with the performance index earlier used for static security assessment. Binary classifier for static security assessment of the power system has been developed using support vector machine. The parameters of Radial Basis Function are selected using teaching learning based optimization method. Sequential forward selection method has been used to select features. The results of the proposed teaching learning based optimization enhanced binary classifier are validated using two IEEE standard test systems.

关键词： static security feature selection grid search teaching learning based optimization composite security index

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A novel hybrid Extreme learning Machine and teaching–learning-based optimization algorithm for skin cancer detection

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Healthcare Analytics 2023年 3卷 100161-100161页

作者： Priyadharshini, N. Selvanathan, N. Hemalatha, B. Sureshkumar, C. Department of Computer Science Sri GVG Visalakshi College for Women Udumalpet India Department of Information Technology Sona College of Technology Salem India Department of Information Technology KGiSL Institute of Technology Coimbatore India

Skin cancers, such as melanoma, can be difficult to spot in their early stages because they often resemble benign moles. Early detection of melanoma is crucial as it increases the chances of successful treatment and prevents cancer from spreading to other areas of the body. Machine learning algorithms and computer vision techniques are versatile techniques for detecting melanoma. However, current research has limitations, such as inaccurate detection and longer computation times. This paper proposes a novel hybrid Extreme learning Machine (ELM) and teaching–learning-based optimization (TLBO) algorithm as a versatile technique for detecting melanoma. ELM is a single-hidden layer feed-forward neural network that can be trained quickly and accurately, while TLBO is an optimization algorithm used to fine-tune the network's parameters for improved performance. Together, these techniques can classify skin lesions as benign or malignant images, potentially improving melanoma detection accuracy. © 2023 The Author(s)

关键词： Extreme learning classifier Improved weighted fuzzy C-Means algorithm Neural networks Segmentation Skin cancer teaching learning based optimization

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Performance enhancement of magnetic levitation system using teaching learning based optimization

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ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 2427-2433页

作者： Yadav, Shekhar Verma, Santosh Kumar Nagar, Shyam Krishna MMM Univ Technol Dept Elect Engn Gorakhpur 273010 Uttar Pradesh India Banaras Hindu Univ Dept Elect Engn Indian Inst Technol Varanasi 221005 Uttar Pradesh India

This paper demonstrates the potency of evolution based optimization techniques in the sense of enhancing the system's performance. teaching learning based optimization (TLBO) is a well-known evolutionary algorithm used to optimize the parameters of the PID controller so as to improve the performance of the magnetic levitation system. The TLBO search algorithm is split into two phases, the teacher phase and the learner phase. The teacher phase is comprised of having minimum performance index as compared to learner phase. The learners improve their knowledge on the basis of teacher's performance. The parameters are tuned while minimizing the performance index of the system. The performance index incorporated in this paper is the integral time weighted square error (ITSE). The corroboration of the above technique is ended by comparing it with the conventional control techniques. (C) 2017 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Magnetic levitation system PID controller teaching learning based optimization Performance index

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Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2018年第11期43卷 1-11页

作者： Suganthi, S. T. Devaraj, D. Thilagar, S. Hosimin Ramar, K. Sri Shakthi Inst Engn & Technol Dept EEE Coimbatore Tamil Nadu India Kalasalingam Univ Dept EEE Krishnan Koil India Anna Univ Dept EEE Madras Tamil Nadu India Multimedia Univ Fac Engn Cyberjaya Malaysia

This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved teaching learning based optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a two-fold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

关键词： Congestion management generation rescheduling generator sensitivity factor teaching learning based optimization restructured power systems

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An energy efficient teaching learning based optimization approach for common content distribution in mobile ad hoc networks

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 72卷 296-306页

作者： Maiti, Mahendra Mukherjee, Srijit Thakurta, Parag Kumar Guha Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 India

An energy efficient content distribution technique in mobile ad hoc networks is proposed in this paper. based on bit rate, remaining energy and maximum allowable delay, a fitness value is evaluated for each node in the network. A set of nodes having maximum fitness values are elected as cluster heads. It is determined by applying teaching learning based optimization technique, only after the nodes satisfy an energy constraint. The clusters of nodes are formed by assigning the remaining nodes to their corresponding cluster heads. The cluster heads can receive the required packets from the base station via long range communication and forward the same to the cluster members. Efficient energy expenditure by this approach leads to an increased network lifetime. The experimental results show effectiveness of the proposed approach over K-means Clustering and U+M techniques in terms of reduced energy consumption and improved network lifetime. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Energy Mobile terminals Cluster heads Base station Bit rate teaching learning based optimization Lifetime

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Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

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TELECOMMUNICATION SYSTEMS 2018年第2期67卷 163-178页

作者： Sharma, Gaurav Kumar, Ashok Natl Inst Technol Dept Elect & Commun Engn Hamirpur 177005 Himachal Prades India

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

关键词： Correction factor DV-Hop algorithm Localization optimization teaching learning based optimization Wireless sensor networks

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