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Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

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WIND ENGINEERING 2023年第2期47卷 464-490页

作者： Maheshwari, Ankur Sood, Yog Raj Jaiswal, Supriya Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur 177005 Himachal Prades India

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The teaching learning based optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

关键词： Optimal power flow teaching-learning-based optimization renewable energy sources renewable uncertainty cost and emission voltage profile enhancement transmission losses

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Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system

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RESULTS IN CONTROL AND optimization 2024年 14卷

作者： Sulaiman, Mohd Herwan Mustaffa, Zuriani Univ Malaysia Pahang Al Sultan Abdullah UMPSA Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Al Sultan Abdullah UMPSA Fac Comp Pekan 26600 Pahang Malaysia

This research provides hyper-heuristic methodologies for solving Optimal Power Flow (OPF) issues in power system networks with Flexible AC Transmission Systems (FACTS) devices. OPF can be treated as one of the demanding challenges in the power system operating networks. To address the problems of loss and cost reduction, three types of FACTS devices will be studied in this paper: Static VAR Compensator (SVC), ThyristorControlled Series Compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS). Two high level hyper-heuristic (HHH) approaches, called Exponential Monte Carlo with counter (EMCQ) and randomly select-Only Improving (OI), are employed as high-level metaheuristic to select and leverage the effectiveness of four low-level metaheuristics (LLH). These low-level metaheuristics comprise the Moth-Flame Optimizer (MFO), Barnacles Mating Optimizer (BMO), teaching-learning based optimization (TLBO) and Gradient-based Optimizer (GBO). The usage of HHH solving the OPF problem is tested on the modified IEEE 30 bus system that integrates the thermal generators with the wind power. Findings of the study demonstrated the promising results by HHH which manages to outperform all the selected LLH algorithms.

关键词： ABC artificial bee colony AGPSO GWO autonomous groups particle swarm and grey wolf optimizers ANN artificial neural network BF bacterial foraging BMO Barnacles Mating Optimizer DE differential evolution DG distributed generation EMCQ exponential Monte Carlo with counter FACTS flexible AC transmission system GBO gradient-based optimizer GSA gravitational search algorithm GOA grasshopper optimization algorithm HBO heap-based optimization HHH high level hyper-heuristic IP interior point LLH low-level heuristic LP linear programming MFO moth flame optimization MLAPO modified lightning attachment procedure optimizer NLP non-linear programming NM Nelder–mead OI randomly select-only improving OPF optimal power flow PSO particle swarm optimization PV photovoltaic QP quadratic programming SSA Salp swarm algorithm SHADE success history based adaptive DE SOS symbiotic organisms search SVC static VAR compensator TCPS thyristor-controlled phase shifter TCSC Thyristor-Controlled Series Compensator TLBO teaching-learning-based optimization

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teaching-learning-based optimization: An optimization method for continuous non-linear large scale problems

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INFORMATION SCIENCES 2012年第1期183卷 1-15页

作者： Rao, R. V. Savsani, V. J. Vakharia, D. P. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

An efficient optimization method called 'teaching-learning-based optimization (TLBO)' is proposed in this paper for large scale non-linear optimization problems for finding the global solutions. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods. (C) 2011 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Non-linear optimization problems Benchmarks functions

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Topology optimization of an offshore jacket structure considering aerodynamic, hydrodynamic and structural forces

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 2911-2930页

作者： Savsani, Vimal Dave, Parth Raja, Bansi D. Patel, Vivek Canadore Coll Dept Engn North Bay ON Canada Pandit Deendayal Petr Univ Gandhinagar Gujarat India Indus Univ Ahmadabad Gujarat India

The present work focused on the optimization of offshore wind turbine structure which can sustain different environmental conditions and is of the least cost. Size and topology optimization is carried out for the jacket structure from the National Renewable Energy Laboratory (NREL) [used in the Offshore Code Comparison Collaboration Continuation (OC4) project] by using teaching learning-based optimization (TLBO) algorithm and genetic algorithm (GA). The optimization process is carried out in Matlab along with the time-dependent dynamic wind turbine simulation with the aerodynamic, hydrodynamic and structural forces in the fatigue, aerodynamics, structures, and turbulence code (FAST) from NREL. This is an innovative process which can be used to substitute the time-consuming construction of a wind turbine for its analysis. In this work, both static and dynamic analyses are carried out for simultaneous size and topology optimization. The forces applied to the structure are realistic in nature and fatigue analysis is carried out to ensure that the structure does not fail during its design life. This ensures that the simulation is more accurate and realistic as compared with other analysis. The results showed that the TLBO algorithm is effective compared to GA in terms of size and topology optimization. Further, the other state-of-the art algorithms from the Congress on Evolutionary Computation (CEC) such as differential evolution, LSHADE, multi-operator EA-II, effective butterfly optimizer, and unified differential evolution are also implemented and the comparative results of all the algorithms are presented.

关键词： Offshore structure teaching-learning-based optimization Genetic algorithm Fatigue analysis

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An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems

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INFORMATION SCIENCES 2021年 561卷 304-325页

作者： Pan, Jeng-Shyang Liu, Nengxian Chu, Shu-Chuan Lai, Taotao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou Peoples R China

Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this study, an efficient surrogate-assisted hybrid optimization (SAHO) algorithm is proposed via combining two famous algorithms, namely, teaching-learning-based optimization (TLBO) and differential evolution (DE). The TLBO is focused on global exploration and the DE is concentrated on local exploitation. These two algorithms are carried out alternately when no better candidate solution can be found. Meanwhile, a new prescreening criterion based on the best and top collection information is introduced to choose promising candidates for real function evaluations. Besides, two evolution control (i.e., the generation-based and individual-based) strategies and a top-ranked restart strategy are integrated in the SAHO. Moreover, a local RBF surrogate which does not need too many training samples is employed to model the landscapes of the target function. Sixteen benchmark functions and the tension/compression spring design problem are adopted to compare the proposed SAHO with other state-of-the-art approaches. Extensive comparison results demonstrate that the proposed SAHO has superior performance for solving expensive optimization problems. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Surrogate-assisted Hybrid optimization algorithm teaching-learning-based optimization Differential evolution Expensive problems

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A teaching-learning-based Particle Swarm optimization for Data Clustering

A Teaching-Learning-Based Particle Swarm Optimization for Da...

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International Conference on Machine Intelligence and Signal Processing (MISP)

作者： Kushwaha, Neetu Pant, Millie Indian Inst Technol Roorkee Dept Appl Sci & Engn Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9789811309236;9789811309229

The present study proposes TLBO-PSO an integrated Teacher-learning-based optimization (TLBO) and Particle Swarm optimization (PSO) for optimum data clustering. TLBO-PSO algorithm searches through arbitrary datasets for appropriate cluster centroid and tries to find the global optima efficiently. The proposed TLBO-PSO is analyzed on a set of six benchmark datasets available at UCI machine learning repository. Experimental result shows that the proposed algorithm performs better than the other state-of-the-art clustering algorithms.

关键词： teaching-learning-based optimization K-means Clustering Particle swarm optimization

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teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

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ENGINEERING optimization 2012年第12期44卷 1447-1462页

作者： Rao, R. V. Savsani, V. J. Balic, J. SV Natl Inst Technol Dept Mech Engn Surat Gujarat India Univ Maribor Fac Mech Engn Maribor Slovenia

An efficient optimization algorithm called teaching-learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, epsilon-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.

关键词： real-parameter optimization unconstrained benchmark functions constrained benchmark functions teaching-learning-based optimization

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Finite element method coupled with TLBO for shape control optimization of piezoelectric bimorph in COMSOL Multiphysics

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2021年第9期97卷 635-644页

作者： Sumit Shukla, Rahul Sinha, A. K. Raja Ramanna Ctr Adv Technol Synchrotrons Utilizat Sect Room 07 Indore 452013 MP India Homi Bhabha Natl Inst Mumbai Maharashtra India Univ Petr & Energy Studies Sch Engn Dept Phys Dehra Dun Uttarakhand India

Finite element methods (FEMs) are more advantageous for analyzing complex geometry and structures than analytical methods. Local search optimization techniques are suitable for the unimodal problem because final result depends on the starting point. On the other hand, to optimize the parameters of multi-minima/maxima problems, global optimization-based FEM is used. Unfortunately, global optimization solvers are not present in, COMSOL Multiphysics, a versatile tool for solving varieties of problems using FEM. teaching-learning-based optimization (TLBO) is a global optimization technique and does not require any algorithm-specific parameter. In this paper, FEM is coupled with TLBO algorithms in COMSOL Multiphysics for solving the global optimization problem. The TLBO algorithm is implemented in COMSOL Multiphysics using the JAVA application programming interface and tested with the standard benchmark functions. The solutions of the standard benchmark problem in COMSOL Multiphysics are in close agreement with the results presented in literature. Furthermore, the optimization procedure thus established is used for the optimization of actuator voltage for piezoelectric bimorphs to achieve the desired shapes. The FEM-based TLBO method is compared with two optimization methods present in COMSOL Multiphysics for a shape control problem;(i) method of moving asymptotes (MMA) and (ii) Bound optimization BY Quadratic Approximation (BOBYQA). The root mean square error shows that the FEM-based TLBO algorithm converges to a global minimum and gives the same result (19.3 nm) at multiple runs, whereas MMA and BOBYQA trapped in local minimum and gave different results for different starting points.

关键词： Global optimization teaching-learning-based optimization finite element method shape control piezoelectric bimorph MMA BOBYQA COMSOL Multiphysics MATLAB JAVA

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On Optimizing the Structure of Neural Networks Through a Compact Codification of Their Architecture 1

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14th International Conference on Metaheuristics (MIC)

作者： Lupion, Marcos Cruz, N. C. Paechter, B. Ortigosa, P. M. Univ Almeria Dept Informat CeiA3 Excellence Agrifood Campus Almeria Spain Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain Edinburgh Napier Univ Sch Comp Edinburgh Midlothian Scotland

ISBN: (数字)9783031265044

ISBN: (纸本)9783031265037;9783031265044

Neural networks stand out in Artificial Intelligence for their capacity of being applied to multiple challenging tasks such as image classification. However, designing a neural network to address a particular problem is also a demanding task that requires expertise and time-consuming trial-and-error stages. The design of methods to automate the designing of neural networks define a research field that generally relies on different optimization algorithms, such as population metaheuristics. This work studies utilizing teaching-learning-based optimization (TLBO), which had not been used before for this purpose up to the authors' knowledge. It is widespread and does not have specific parameters. Besides, it would be compatible with deep neural network design, i.e., architectures with many layers, due to its conception as a large-scale optimizer. A new encoding scheme has been proposed to make this continuous optimizer compatible with neural network design. This method, which is of general application, i.e., not linked to TLBO, can represent different network architectures with a plain vector of real values. A compatible objective function that links the optimizer and the representation of solutions has also been developed. The performance of this framework has been studied by addressing the design of an image classification neural network based on the CIFAR-10 dataset. The achieved result outperforms the initial solutions designed by humans after letting them evolve.

关键词： Artificial intelligence Neural network architecture optimization Meta-heuristics teaching-learning-based optimization

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Discovery of Botnet Activities in Internet-of-Things System Using Dynamic Evolutionary Mechanism

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NEW GENERATION COMPUTING 2022年第1期40卷 255-283页

作者： Shukla, Alok Kumar Dwivedi, Shubhra VIT AP Univ Sch Comp Sci & Engn Amaravati Andhra Pradesh India

The rapid growth in numerous technological aspects of computer networks and various lightweight protocols have endorsed the concept of the Internet of things. Despite pervasive concern about cyber-attacks and privacy violation issues, nowadays most of the research scientists are using Internet of Things (IoT) devices in broad way like home automation, and smart mobility for malicious and intrusion traffic identification. For securing machines and connecting people to valuable resources in IoT networks, in this study, we have proposed dynamic multi-population teaching-learning-based optimization algorithm, called DMPTLBO to protect against malicious intruders in network system. This work utilizes dynamic scheme for dividing the learner into sub-population to balance the exploration and exploitation of the search process based on the problem landscape. Furthermore, search information is shared and diffused among different sub-population to maintain the diversity and enhance the exploration process to escape high false alarm rate and low detection rate. Moreover, purposeful detecting strategy is used for maintaining accessibility, and interoperability based on historical information of the search process. The performance of the proposed method is evaluated by series of comprehensive computational experiments and comparing it with state-of-the-art algorithms obtainable for identifying attacks on BoT-IoT and UNSW-NB15 datasets. Experimental results show that the proposed model is significantly achieving higher performance compared to other state-of-art techniques in terms of classifier accuracy, detection rate, false alarm rate, and CPU time.

关键词： Internet of things Bonet Intrusion detection Multi-population teaching-learning-based optimization Artificial intelligence

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