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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Ragini Kumar, Sudhanshu Akash Nath, Apurv Singh, Shivanshu Kumar Awanish Das, Pallav Bhattacharya, Aniruddha Natl Inst Technol Dept Elect Engn Agartala 799055 India

ISBN: (纸本)9781467365031

In a large power system, the optimal control of the frequency and the active power is highly desired. For many practical purposes the required operation of the controllers in maintaining the healthy power system becomes a vital part. After performing power and frequency control within the permissible limit, a better appreciation of the operation of power systems can be obtained. It is extremely necessary that the output of the machines at any instant will be exactly equal to the load plus transmission and distribution losses on the system. If the output is higher than the demand, the machines will tend to accelerate and the frequency will rise, and vice versa. Hence the frequency is not a constant quantity, but continuously varies. These variations are normally small and have no noticeable effect on most consumers. The frequency is continuously monitored against standard time-sources and when long-term tendencies to rise or fall of frequency is noticed, the control engineers take appropriate action to regulate the generators' outputs. A continuous time transfer function model of the system for studying dynamic response for small load disturbances is presented here. A proportional-integral-derivative (PID) controller based automatic generation control scheme is applied. There are different algorithms to find out the optimum parameters of a controller like Genetic algorithm, Particle swarm optimization (PSO), backtracking search algorithm (BSA) etc. However, in this project work, BSA is employed to find optimum settings of PID controller constants for a two area power system network. Results obtained using BSA has been compared with those obtained using PSO.

关键词： Automatic Generation control backtracking search algorithm PID controller Thermal power system

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A New Hybridized backtracking search Optimization algorithm with Differential Evolution for Sidelobe Suppression of Uniformly Excited Concentric Circular Antenna Arrays

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2015年第3期25卷 262-268页

作者： Das, Sudipta Mandal, Durbadal Kar, Rajib Ghoshal, Sakti Prasad Natl Inst Technol Dept Elect & Commun Engn Durgapur India Natl Inst Technol Dept Elect Engn Durgapur India

In this work, a hybridized combination of backtracking search algorithm (BSA) with differential evolution (DE) is proposed and applied on sidelobe suppression problem of uniformly excited concentric circular antenna arrays (CCAA). Each array is assumed to have isotropic elements and be placed on x-y plane and has one center element. A search for optimal setting of ring radius and number of elements in each ring is carried out so as to achieve low sidelobe performance on overall azimuth plane. Care has been taken so that directivity does not get degraded as far as possible. Before applying this algorithm to CCAA design problem, BSA and the hybridized algorithm BSA-DE are tested on five complex benchmark functions. Based on 30-independent runs for each benchmark function, Wilcoxon's pairwise signed ranks test is utilized to judge the relative search performance of these two algorithms. The hybridized algorithm proves its superiority on almost all the considered benchmark functions. For the CCAA design problem dealt with in this work, BSA-DE shows its superiority on one or both pattern parameters as well as structural parameters. (c) 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:262-268, 2015.

关键词： backtracking search algorithm differential evolution mutation operator concentric circular antenna arrays sidelobe suppression

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Solving non-convex economic dispatch problem via backtracking search algorithm

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ENERGY 2014年 77卷 372-381页

作者： Modiri-Delshad, Mostafa Abd Rahim, Nasrudin Wisma R&D Univ Malaya UM Power Energy Dedicated Adv Ctr UMPE DAC Kuala Lumpur 59990 Malaysia King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia

This paper presents BSA (backtracking search algorithm) for solving of ED (economic dispatch) problems (both convex and non-convex) with both the valve-point effects in the generator cost function and the transmission network loss considered. BSA is a new evolutionary algorithm for solving of numerical optimization problems;it uses a single control parameter and two crossover and mutation strategies for powerful exploration of the problem's search space. Four test systems (with 3, 6, 20, and 40 generators) are the case studies verifying the method's robustness and effectiveness. The results confirm that compared with existing well-known methods and especially in large-scale test systems, the proposed algorithm is the better approach to solving ED problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch backtracking search algorithm Valve point effect Transmission loss Non-convex Evolutionary

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A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2014年第21期39卷 11165-11174页

作者： Askarzadeh, Alireza Coelho, Leandro dos Santos Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn BR-80060000 Curitiba Parana Brazil

Polarization curves are vital tools for simulation, evaluation, management, and optimization of proton exchange membrane fuel cells (PEMFCs). They are affected by various parameters related to the nonlinearities associated with the electrochemical processes governing fuel cells (FCs). However, the relative importance and effect of each parameter on the polarization curve is different. In order to efficiently estimate the unknown parameters of the PEMFC electrochemical-based model and obtain accurate solutions, in this paper, a backtracking search algorithm combined with Burger's chaotic map (BSABCM) is proposed and investigated. The results related to two real systems, the SR-12 Modular PEM Generator and the Ballard Mark V FC, illustrate the effectiveness of the proposed methodology. Furthermore, the BSABCM presented competitive results when compared with the results of the other metaheuristics. Copyright (C) 2014, Hydrogen Energy Publications, LLC: Published by Elsevier Ltd. All rights reserved.

关键词： Proton exchange membrane fuel cells backtracking search algorithm Parameter estimation

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An Improved backtracking search algorithm for Constrained Optimization Problems 7

An Improved Backtracking Search Algorithm for Constrained Op...

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7th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Zhao, Wenting Wang, Lijin Yin, Yilong Wang, Bingqing Wei, Yi Yin, Yushan Shandong Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China

ISBN: (纸本)9783319120966;9783319120959

backtracking search algorithm is a novel population-based stochastic technique. This paper proposes an improved backtracking search algorithm for constrained optimization problems. The proposed algorithm is combined with differential evolution algorithm and the breeder genetic algorithm mutation operator. The differential evolution algorithm is used to accelerate convergence at later iteration process, and the breeder genetic algorithm mutation operator is employed for the algorithm to improve the population diversity. Using the superiority of feasible point scheme and the parameter free penalty scheme to handle constrains, the improved algorithm is tested on 13 well-known benchmark problems. The results show the improved backtracking search algorithm is effective and competitive for constrained optimization problems.

关键词： constrained optimization backtracking search algorithm differential evolution algorithm breeder GA mutation operator mutation

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An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market

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IEEE ACCESS 2024年 12卷 43674-43690页

作者： Pourdaryaei, Alireza Mohammadi, Mohammad Muhammad, Munir Azam Bin Fakhrul Islam, Junaid Karimi, Mazaher Shahriari, Amidaddin Shiraz Univ Sch Elect & Comp Engn Dept Power & Control Shiraz *** Iran Univ Hormozgan Dept Elect & Comp Engn Bandar Abbas *** Iran Iqra Univ Dept Elect Engn Main Campus Karachi 75300 Pakistan Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland Islamic Azad Univ Dept Elect Engn South Tehran Branch Tehran *** Iran

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. backtracking search algorithm as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information and support vector machine. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

关键词： backtracking search algorithm electricity market electricity price forecasting feature selection support vector machine

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Positioning multiple optical network units in fiber-wireless networks: An efficient hybrid K-harmonic means clustering approach

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OPTICAL FIBER TECHNOLOGY 2024年 84卷

作者： Emami, Hojjat Pashazadeh, Saeid Univ Bonab Dept Comp Engn Bonab Iran Univ Tabriz Fac Elect & Comp Engn Dept IT Tabriz Iran

Wireless-optical broadband access network (WOBAN) or fiber-wireless (FiWi) is a robust and cost-effective solution to provide high bandwidth network connectivity to mobile subscribers. Optical network unit (ONU) placement is a topological optimization problem that plays a vital role in effective deployment of FiWi network. Recently, some artificial intelligence-based approaches for positioning ONUs were proposed and generated promising results. However, the reported performance in the literature is far from the ideal state. This issue emphasizes the necessity of introducing new placement methods or improving the performance of existing methods. This paper presents a hybrid clustering algorithm for finding the optimal position of multiple ONUs. The proposed backtracking K-harmonic means (BKHM) is an integration of K-harmonic means (KHM) and a backtracking search algorithm (BSA). BKHM not only efficiently specifies the optimal positions of ONUs but also significantly decreases the implementation cost. To demonstrate the performance of the BKHM, we evaluate it on well-known placement benchmarks compared with BSA, KHM, whale optimization algorithm (WOA), enhanced backtracking search (EBS), harris hawks optimization (HHO), and chaotic local search-based levy flight distribution (CLF) algorithms. The experimental results and statistical analysis proved that the proposed BKHM algorithm outperformed its counterparts on most benchmarks in terms of cost optimization, convergence accuracy, and solution quality.

关键词： backtracking search algorithm Clustering Fiber-wireless network K-harmonic means Optical network unit placement

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Stability improvement of the PSS-connected power system network with ensemble machine learning tool

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ENERGY REPORTS 2022年 8卷 11122-11138页

作者： Shahriar, M. S. Shafiullah, M. Pathan, M. I. H. Sha'aban, Y. A. Bouchekara, Houssem R. E. H. Ramli, Makbul A. M. Rahman, M. M. Univ Hafr Al Batin Dept Elect Engn Hafar al Batin 31991 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Renewable Energy & Power Dhahran 31261 Saudi Arabia Hajee Mohammad Danesh Sci & Technol Univ Dept Elect & Elect Engn Dinajpur Bangladesh King Abdulaziz Univ Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Hamad Bin Khalifa Univ Coll Sci & Engn Informat & Comp Technol Div Doha Qatar

Stability is a primary requirement of the electrical power system for its flawless, secure, and economical operation. Low-frequency oscillations (LFOs), commonly seen in interconnected power systems, initiate the possibility of instability and, therefore, require sophisticated care to deal with. This paper proposes an original approach to tuning the parameters of the power system stabilizer (PSS), which plays a crucial role in the power system networks to dampen unwanted oscillations. The ensemble method combines multiple machine learning techniques and has been used for tuning the PSS parameters in real-time for two PSS-connected power system networks. The first system is a single-machine infinite bus power system, while the second is a unified power flow controller (UPFC) device. The backtracking search algorithm (BSA) based proposed ensemble model is formed by combining three machine learning (ML) techniques, namely the extreme learning machine (ELM), neurogenetic (NG) system, and multi-gene genetic programming (MGGP). To validate the stability of the network, Eigenvalues, well-recognized statistical parameters, and minimum damping ratios were analyzed, besides the time-domain simulation results. Furthermore, results for various loading conditions were prepared to check the robustness of the proposed model. A comparative study of the proposed approach with NG, ELM, MGGP models, and two reference cases along with the conventional method will validate the superiority of the employed ML approach. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Artificial intelligence backtracking search algorithm Ensemble method Extreme learning machine Genetic programming Low-frequency oscillation Neurogenetic Power system stability Real-time

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A new meta-heuristic programming for multi-objective optimal power flow

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ELECTRICAL ENGINEERING 2021年第2期103卷 1217-1237页

作者： Daqaq, Fatima Ouassaid, Mohammed Ellaia, Rachid Mohammed V Univ Mohammadia Sch Engineers Engn Smart & Sustainable Syst Res Ctr Rabat Morocco Mohammed V Univ Mohammadia Sch Engineers Lab Study & Res Appl Math Rabat Morocco

In this paper, a new multi-objective approach is suggested, known as multi-objective backtracking search algorithm (MOBSA) in order to formulate and solve the optimal power flow (OPF) problem in power systems. Many objective functions are considered like fuel cost, power losses, and voltage deviation. The structure of the proposed method is simple and has one control parameter. In addition, MOBSA is able to solve the highly constrained objectives. A fuzzy membership technique is integrated into the BSA algorithm to extract the best compromise solution from all the obtained Pareto optimal solutions. Furthermore, the capability of the MOBSA approach is evaluated and verified for bi- and tri-objectives, and tested on three standard IEEE power systems, small network 30-bus, medium network 57-bus, and large network 118-bus test systems. The obtained results reveal that the proposed method is efficient to generate well-distributed Pareto optimal non-dominated solutions. Likewise, the comparison analysis with some re-implemented methods as MODE, SPEA, MALO, and those found in the literature as MOABC/D, QOTLBO, NSGA-II and NSMOGSA, assured the superiority, effectiveness, and robustness of MOBSA.

关键词： Power system Optimal power flow Multi-objective optimization backtracking search algorithm Fuzzy membership

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Optimization-Based Fuzzy Energy Management Strategy for PEM Fuel Cell/Battery/Supercapacitor Hybrid Construction Excavator

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY 2021年第4期8卷 1267-1285页

作者： Dao, Hoang Vu To, Xuan Dinh Truong, Hoai Vu Anh Do, Tri Cuong Ho, Cong Minh Dang, Tri Dung Ahn, Kyoung Kwan Univ Ulsan Grad Sch Mech Engn 93 Daehak Ro Ulsan 680749 South Korea Univ Ulsan Sch Mech Engn 93 Daehak Ro Ulsan 680749 South Korea Ho Chi Minh City Univ Technol & Educ Dept Mechatron Ho Chi Minh City 700000 Vietnam

Fuel cell hybrid electric construction equipment (FCHECE) is known as a promising solution to achieve the goal of energy saving and environment protection. Energy management strategy is a key technology of FCHECE, which splits the energy flow between power sources. This paper presents a novel optimal energy management strategy for a hybrid electric-powered hydraulic excavator system to enhance power performance, power sources lifespan, and fuel economy. As for the proposed powertrain configuration, fuel cell serves as a primary energy source, and supercapacitor and battery are considered as energy storages. The integration of supercapacitor and battery in fuel cell vehicle has advantages of improving power performance and storing the regenerative energy for future usage. An energy management strategy based on fuzzy logic control and a rule-based algorithm is proposed to effectively distribute the power between the three sources and reuse the regenerative energy. Furthermore, the parameters of the fuzzy logic system are optimized using the combination of a backtracking search algorithm which provides a good direction to the global optimal region and sequential dynamic programming as a local search method to fine-tune the optimal solution in order to reduce the hydrogen consumption and prolong the lifetime of the power sources. Simulation results show that the proposed energy management strategy enhances the vehicle performance, improves fuel economy of the FCHECE by 10.919%, increase battery and supercapacitor charge-sustaining capability as well as efficiency of the fuel cell system.

关键词： PEM fuel cell Energy management strategy Construction excavator Fuzzy logic backtracking search algorithm Sequential quadratic programming

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