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Transmission congestion management with integration of wind farm: a possible solution methodology for deregulated power market

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2020年第2期11卷 287-296页

作者： Gope, Sadhan Goswami, A. K. Tiwari, P. K. Mizoram Univ Elect Engn Dept Aizawl 796004 India Natl Inst Technol Silchar Elect Engn Dept Silchar 788010 India

Congestion management (CM) work is a challenging task for researchers working in the field of power transmission sector. In this paper, an appreciable effort has been made to eliminate the line congestion by integrating wind farm in the system. To reschedule the conventional generators for achieving best optimal solution, moth flame optimization (MFO) algorithm is implemented here. Generator sensitivity factors and bus sensitivity factors are respectively used to reschedule the generators and to optimally locate the wind farm in deregulated power system. To test the performance and check the effectiveness of the proposed CM approach, modified IEEE 30 bus test system and modified 39 bus New England test system are used here. Further after obtained details results, the competitive performance of MFO algorithm is compared and verified with others optimization algorithms like artificial bee colony, firefly algorithm and ant lion optimizer algorithms in terms of rescheduling amount, rescheduling cost and active power losses.

关键词： Wind farm moth flame optimization algorithm Generator sensitivity factor Bus sensitivity factor

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A hybrid SA-MFO algorithm for function optimization and engineering design problems

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COMPLEX & INTELLIGENT SYSTEMS 2018年第3期4卷 195-212页

作者： Sayed, Gehad Ismail Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt SRGE Cairo Egypt

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

关键词： Simulated annealing algorithm moth flame optimization algorithm Function optimization Constrained optimization problems

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A Nonlinear Grey Bernoulli Model with Conformable Fractional-Order Accumulation and Its Application to the Gross Regional Product in the Cheng-Yu Area

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Journal of Systems Science and Information 2024年第2期12卷 245-273页

作者： Wenqing WU Xin MA Bo ZENG Yuanyuan ZHANG School of Science Civil Aviation Flight University of ChinaDeyang 618307China School of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China College of Business Planning Chongqing Technology and Business UniversityChongqing 400067China

This study considers a nonlinear grey Bernoulli forecasting model with conformable fractionalorder accumulation,abbreviated as CFNGBM(1,1,λ),to study the gross regional product in the ChengYu *** new model contains three nonlinear parameters,the power exponentγ,the conformable fractional-orderαand the background valueλ,which increase the adjustability and flexibility of the CFNGBM(1,1,λ)*** parameters are determined by the moth flame optimization algorithm,which minimizes the mean absolute prediction percentage *** CFNGBM(1,1,λ)model is applied to the gross regional product of 16 cities in the Cheng-Yu area,which are Chongqing,Chengdu,Mianyang,Leshan,Zigong,Deyang,Meishan,Luzhou,Suining,Neijiang,Nanchong,Guang’an,Yibin,Ya’an,Dazhou and *** data from 2013 to 2021,several grey models are established and results show that the new model has higher accuracy in most cases.

关键词： nonlinear grey Bernoulli model conformable fractional-order operator moth flame optimization algorithm gross regional product the Cheng-Yu area

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Improved market prediction using meta-heuristic algorithms and time series model and testing market efficiency

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Iran Journal of Computer Science 2023年第1期6卷 29-61页

作者： Shahvaroughi Farahani, Milad Farrokhi-Asl, Hamed Department of Finance Faculty of Management Khatam University Tehran Iran Sheldon B. Lubar College of Business University of Wisconsin-Milwaukee Milwaukee WI United States

This study aims to evaluate the efficiency of using technical indicators such as closing price, lowest price, highest price, and exponential moving average in the prediction of stock prices. We use a genetic algorithm (GA) and a hybrid of grey wolf optimization and particle swarm optimization binary algorithm (GWO-PSO) as feature selection methods. In addition, we train our neural network by using some metaheuristic algorithms such as harmony search algorithm (HS), particle swarm optimization algorithm (PSO), modified particle swarm optimization algorithm (MPSO), modified particle swarm optimization algorithm with time-varying acceleration coefficients (MPSO-TVAC), moth flame optimization (MFO), wolf optimization algorithm (WOA) and chimp optimization algorithm (ChOA). The experimental results show that using metaheuristic algorithms to fortify neural networks may increase their ability in finding optimal solutions. We also compare the results of our proposed algorithms with the results of the autoregressive integrated moving average (ARIMA) model. To compare the performance of the proposed algorithms and select the best one, we introduce eight estimation criteria for error assessment. Moreover, market efficiency is another important factor that is checked in this paper to avoid abnormal returns. Briefly speaking, it is the first time that ChOA and MFO algorithms have been used for the prediction of stock prices and to improve ANN. In addition, we use two algorithms (i.e., GA and GWO-PSO) for improving the feature selection process. Finally, experimental results show that WOA has the best performance among applied algorithms. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishi

关键词： Artificial neural network Chimp optimization algorithm Feature selection Meta-heuristic algorithms moth flame optimization algorithm Technical indicators

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Research on Battery Energy Storage as Backup Power in the Operation optimization of a Regional Integrated Energy System

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ENERGIES 2018年第11期11卷 2990-2990页

作者： Li, Jianfeng Niu, Dongxiao Wu, Ming Wang, Yongli Li, Fang Dong, Huanran North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China

Recently, integrated energy systems have become a new type of energy supply model. It is clear that integrated energy systems can improve energy efficiency and reduce costs. However, the use of a battery energy storage system (BESS) as a backup power source will affect the operating costs of a regional integrated energy system (RIES) in different situations. In this paper, a regional integrated energy system including wind turbines, photovoltaics, gas turbines and battery energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plans of each unit were optimized by a moth flame optimization (MFO) algorithm. Finally, three different scenarios were proposed for the simulation optimization. The simulation optimization results show that when the BESS is used as a backup power source, the operating cost of the system and the resulting pollutant emissions are less than the diesel generator (DG) set. Therefore, it is worthwhile to use BESS instead of DG as the backup power source in RIES.

关键词： integrated energy system operating optimization battery energy storage renewable energy backup power moth flame optimization algorithm

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Isogeometric material optimization for shape control of bi-directional functionally graded plates with piezoelectric layers

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THIN-WALLED STRUCTURES 2024年 202卷

作者： Ma, Liangliang Wang, Chao Chong, Yun Hu, Wenfeng Zeng, Lei Anhui Polytech Univ Engn Res Ctr Anhui Green Bldg & Digital Construct Wuhu 241000 Anhui Peoples R China Anhui Polytech Univ Key Lab Mech Wuhu 241000 Anhui Peoples R China Anhui Univ Technol Dept Mech Engn Maanshan 243002 Anhui Peoples R China Nantong Taisheng Blue Isl Offshore Co Ltd Qidong 226200 Jiangsu Peoples R China

This paper proposes an effective numerical method for shape control of bi-directional functionally graded plates (2D-FGPs) with piezoelectric layers. Isogeometric analysis (IGA) based on non -uniform rational B -splines (NURBS) related to third -order shear deformation theory (TSDT) is employed for the static analysis of the 2D-FGPs with piezoelectric layers. The B -spline basis functions are utilized to represent the distribution of the ceramic volume fractions, where the control points placed along the plane corresponding to the ceramic volume fraction and the applied voltages are taken as the design variables. In addition, an improved moth flame optimization algorithm is utilized to solve the optimization problem of minimizing the static shape error, which effectively balances the exploratory and exploitative capabilities of the algorithm. Various numerical examples of square, skew, and dart -shaped 2D-FGPs are analyzed to validate the proposed method and demonstrated the superior mechanical performance of 2D-FGPs over 1D-FGPs.

关键词： Shape control Bi-directional functionally graded plates with piezoelectric layers Isogeometric analysis moth flame optimization algorithm

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Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期227卷 120367页

作者： Sahoo, Saroj Kumar Houssein, Essam H. Premkumar, M. Saha, Apu Kumar Emam, Marwa M. Natl Inst Technol Agartala Dept Math Agartala 799046 India Minia Univ Fac Comp & Informat Al Minya Egypt Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed to-mography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self -adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm.

关键词： moth flame optimization algorithm Metaheuristics COVID-19 CT images Multilevel thresholding Image segmentation

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FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques

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SUSTAINABLE CITIES AND SOCIETY 2022年第0期80卷 103825-103825页

作者： Talaat, M. Alblawi, Adel Tayseer, M. Elkholy, M. H. Zagazig Univ Fac Engn Elect Power & Machines Dept PO Zagazig 44519 Egypt Shaqra Univ Coll Engn Mech Engn Dept PO Ar Riyadh 11911 Saudi Arabia Egyptian Chinese Univ Fac Engn & Technol PO Cairo 11787 Egypt

Despite the merits of Renewable Energy Sources (RES) as clean and reliable alternatives for electrical energy generation, there are some problems related to their highly cost and low efficient operation with non - linear loads. As one of the most vital methodologies to improve the operational quality of RES within the electrical network, the integration of RES in a one hybrid system can effectively contribute to overcome the obstacles of the randomness and inability to accurately predict the daily generation of each individual RES. In this research, Fuel Cells (FCs) are practically integrated with two renewable sources (wave energy and solar energy) utilizing the Field Programmable Gate Array (FPGA) as a new innovative digital controller technique. FPGAs are chosen in this study for its ultra - fast processing speed that is expected to reach to almost 100 MHz and its higher response than other microcontrollers for RES integration. Although the merits of FPGAs like fast response, having no processors and behaving in a parallel manner, there are some obstacles in controlling the energy level of RES during the integration process. To overcome the problems of FPGAs, moth flame optimization (MFO) algorithm is utilized with Artificial Neural Network (ANN) to enhance its operational accuracy for providing an effective and precise forecasting control scenario for the proposed hybrid system. In this paper, FCs are provided as an effective Battery Energy Storage Systems (ESS) to overcome the sudden operational outage of any RES to ensure the reliability of the proposed hybrid system within the electrical network. This research provides the hybrid combination between the Buck - Boost converter and FPGA as a vital approach to adjust the voltage level of the proposed RES integration within a reasonable value. In this research, all the obtained results are assessed based on the available previous simulation and empirical data to confirm the validity and high response of the FPGA

关键词： Field Programmable Gate Array Fuel Cells Wave Energy Solar Energy Buck Boost Artificial Neural Network moth flame optimization algorithm

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A Performance Study of Bio-Inspired algorithms in Autonomous Landing of Unmanned Aerial Vehicle

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Procedia Computer Science 2020年 171卷 1449-1458页

作者： Harun Surej Ilango Ramanathan R. Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham– 641112 India

The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial one. During landing, it is important for the UAV to maintain a constant speed and glide slope to ensure the stability and a successful touchdown on the runway. Also, it is important for a UAV to estimate the accurate point of landing in a minimal amount of time. Embedding Bio-inspiring algorithms in UAV control systems helps in accurate estimation of the landing point in a minimal amount of time. In this research work, the Bio-inspired optimization algorithms Bats optimization algorithm, moth flame optimization algorithm and Artificial Bee Colony algorithm are used in determining the coordinates (points) of the computed path and to determine the optimal point of landing which ensures the above said parameters are within the operational limits of the UAV. The objective of this research work is to determine the path from the computed points and to find the optimal landing point in a minimal amount of time. The difference between the original points of the actual path and the derived computed points of the estimated path is measured as the error rate. The performance of the algorithms is analyzed in terms of two trade-off parameters, the time taken to compute the landing point and the accuracy in predicting the landing point. The empirical results show that the moth flame optimization algorithm takes less time to compute the optimal point with minimal error among the three optimization algorithms taken up for the study.

关键词： Fixed-wing Unmanned Aerial Vehicles (UAVs) Bio-inspired optimization algorithms Bats optimization algorithm moth flame optimization algorithm Artificial Bee Colony algorithm

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