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A demand side management control strategy using whale optimization algorithm

SN APPLIED SCIENCES 2019年第8期1卷 870页

作者： Sharma, Ankit Kumar Saxena, Akash Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur 302017 Rajasthan India

In recent years, demand side management programs are in the spotlight due to the evolution of the smart grid and consumer-centric policies. Demand side management program contains many objectives one of the prime objective is to manage energy demand by certain change in consumer demand. This can be achieved by various methods such as financial discount and change in behavior through imparting education to support the stressed conditions of the grid. This paper demonstrates demand side management strategies based upon strategic conservation, peak clipping and load shifting techniques for future smart grids. The grid contains large number of controllable devices. The day before strategic conservation, peak clipping and load shifting techniques discussed in this paper are mathematically derived for minimization problem. A heuristic-based whale optimization algorithm (WOA) was developed for solving this problem of minimization. Simulations are conducted on a test smart grid that contains a variation in loads in two service areas, one with residential consumers, and another with commercial consumers. WOA proves its efficacy by comparing the results with spider monkey optimization and biogeography based optimization. The simulation results show that proposed demand side management strategies achieve substantial savings, while reducing the peak load demand of the smart grid.

关键词： Demand side management Demand response whale optimization algorithm Load shifting Strategic conservation and peak clipping Spider monkey optimization and biogeography based optimization

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optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Levy flight and distribution enhancement technique

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2021年第1期15卷 1682-1702页

作者： Lai, V Huang, Y. F. Koo, C. H. Ahmed, Ali Najah El-Shafie, Ahmed Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia Univ Tenaga Nasl UNITEN Inst Engn Infrastruct IEI Kajang Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

Development, operations and management of multi-objective reservoirs, is vital for timely water supply. Optimisation studies were done at the Klang Gate Dam (KGD) utilising standard optimisation and dynamic programming;according to the technology then. Taking it further, the KGD was studied using the nature-inspired meta-heuristic algorithms (MHAs). The whale Optimisation algorithm (WOA) solves complex technical issues. The Levy flight and distribution (LFWOA) was incorporated to increase productivity at the KGD. The aim of this study to minimise KGD's water deficit with WOA and LFWOA. The study comprises of two sections. The first section examined observed monthly inflow, demand, and storage data from 2001 to 2019, whilst the second compares performances to established MHAs, from 1097 to 2008. In the first section, LFWOA and WOA reliability were 60.53 and 58.33 %,respectively. For 2010, both methods gave similar vulnerability value. The LFWOA scored 1.04 while the WOA scored 1. In second section, the LFWOA satisfied 69.70% of exact demand, while the WOA met 56.06%. LFWOA had attained the least shortage. The LFWOA algorithm was the most robust among the MHAs (1.88). In short, the LFWOA is better on the count of reliability, resilience, and scarcity scores.

关键词： Metaheuristics whale optimization algorithm Levy flight and distribution reservoir operation optimization Klang Gate Dam (KGD)

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whale optimization algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

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ENERGIES 2024年第17期17卷 4300页

作者： Ouyang, Jian Lin, Hao Hong, Ye Guangdong Polytech Normal Univ Ind Training Ctr Guangzhou 510665 Peoples R China Guangdong Polytech Normal Univ Sch Automat Guangzhou 510665 Peoples R China

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

关键词： LMFP battery SOC estimation BP neural network whale optimization algorithm chaotic mapping improving

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A Modified whale optimization algorithm for Pattern Synthesis of Linear Antenna Array

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2021年第5期E104A卷 818-822页

作者： Feng, Wentao Hu, Dexiu Natl Key Lab Sci & Technol Blind Signal Proc Chengdu 610041 86 Sichuan Peoples R China Zhengzhou Inst Informat Sci & Technol Zhengzhou 450002 86 Henan Peoples R China

A modified whale optimization algorithm (MWOA) with dynamic leader selection mechanism and novel population updating procedure is introduced for pattern synthesis of linear antenna array. The current best solution is dynamic changed for each whale agent to overcome premature with local optima in iteration. A hybrid crossover operator is embedded in original algorithm to improve the convergence accuracy of solution. Moreover, the flow of population updating is optimized to balance the exploitation and exploration ability. The modified algorithm is tested on a 28 elements uniform linear antenna array to reduce its side lobe lever and null depth lever. The simulation results show that MWOA algorithm can improve the performance of WOA obviously compared with other algorithms.

关键词： whale optimization algorithm dynamic leader selection null steering sidelobe suppression hybrid crossover

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Performance Evaluation and Sustainability Assessment in Laser Micro-drilling of Carbon Nanotube-Reinforced Polymer Matrix Composite Using MOORA and whale optimization algorithm

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2022年第3期6卷 603-620页

作者： Mishra, Lipsamayee Mahapatra, Trupti Ranjan Mishra, Debadutta Veer Surendra Sai Univ Technol Dept Prod Engn Sambalpur Odisha India

The present work focuses on determining optimal parametric data set and sustainability assessment during laser micro-drilling of a new class of polymer matrix composite consisting of carbon nanotube. The material removal rate, taper, and heat-affected zone during machining are considered as performance measuring indices. Experiments are conducted using Taguchi's L-25 orthogonal array with cutting speed, pulse frequency, lamp current, air pressure, and pulse width as input control parameters. An overall assessment value and ranking of the desired output parameters are carried out using multi-objective optimization based on the ratio analysis (MOORA) method to acquire the best parametric setting. A second-degree regression equation is developed through MOORA-Taguchi, involving all input parameters to perform the multi-objective optimization using whale optimization algorithm which is a meta-heuristic optimization technique. To verify the adequacy of the developed model, analysis of variance tool using response surface methodology is utilized. The optimal results obtained by using the whale optimization algorithm are cutting speed of 150 (m/s), lamp current of 26 (amp), frequency of 12 (kHz), air pressure of 3 (kg/cm(2)), and pulse width of 30 (%) with an objective function value of 0.022715. The used technique is found to be a potential in finding multi-response optimization that can fulfill the wide necessities of process engineers working in the laser industries. It is also demonstrated that the proposed process is easing worker safety, promoting pleasant working environment and better product quality, and enhancing production rate leading to improved sustainability.

关键词： CNT-PMC Laser micro-drilling Taguchi's DOE MOORA method whale optimization algorithm Sustainability

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Mango Pest Detection Using Entropy-ELM with whale optimization algorithm

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Intelligent Automation & Soft Computing 2023年第3期35卷 3447-3458页

作者： U.Muthaiah S.Chitra Department of Computer Science and Engineering Sri Shanmugha College of Engineering and TechnologySankari637304India Department of Computer Science and Engineering Er.Perumal Manimekalai College of EngineeringHosur635117India

Image processing,agricultural production,andﬁeld monitoring are essential studies in the researchﬁ*** diseases have an impact on agricultural production and *** disease detection at a preliminary phase reduces economic losses and improves the quality of *** identifying the agricultural pests is usually evident in plants;also,it takes more time and is an expensive technique.A drone system has been developed to gather photographs over enormous regions such as farm areas and *** atmosphere generates vast amounts of data as it is monitored closely;the evaluation of this big data would increase the production of agricultural *** paper aims to identify pests in mango trees such as hoppers,mealybugs,inﬂorescence midges,fruitﬂies,and stem *** of the massive volumes of large-scale high-dimensional big data collected,it is necessary to reduce the dimensionality of the input for classify-ing *** community-based cumulative algorithm was used to classify the pests in the existing *** proposed method uses the Entropy-ELM method with whale optimization to improve the classiﬁcation in detecting pests in *** Entropy-ELM method with the whale optimization algorithm(WOA)is used for feature selection,enhancing mango pests’classiﬁcation *** Vector Machines(SVMs)are especially effective for classifying while users get var-ious classes in which they are *** are created as suitable classiﬁers to categorize any dataset in Big Data *** proposed Entropy-ELM-WOA is more capable compared to the existing systems.

关键词： whale optimization algorithm Entropy-ELM feature selection pests detection support vector machine mango trees classiﬁcation

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Multi-Objective Real Power Loss and Voltage Deviation Minimization for Grid Connected Micro Power System Using whale optimization algorithm

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INTERNATIONAL ENERGY JOURNAL 2018年第3期18卷 297-310页

作者： Ang, Sovann Leeton, Uthen Kulworawanichpong, Thanatchai Chayakulkeeree, Keerati Suranaree Univ Technol Sch Elect Engn Inst Engn Nakhon Ratchasima 30000 Thailand

In this paper, a whale optimization algorithm (WOA) is applied to solve the multi-objective real power loss and bus voltage deviation (VD) minimizations for grid connected micro power system with non firm small power plants. The control variables are the voltage magnitude at voltage control buses and the transformer tap changers. The methods were tested with IEEE 6 and 14 buses systems comparing to genetic algorithm (GA), artificial bee colony (ABC), and particle swarm optimization (PSO). The simulation results shown that WOA can successfully provide the minimum the multi-objective real power loss and bus VD solution than those computed by GA, ABC, and PSO. Moreover, the applied methods use the minimum computation time among all methods.

关键词： loss minimization optimal power flow voltage deviation whale optimization algorithm

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An Effective Power optimization Approach Based on whale optimization algorithm with Two-Populations and Mutation Strategies

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Chinese Journal of Electronics 2024年第2期33卷 423-435页

作者： Juncai HE Zhenxue HE Jia LIU Yan ZHANG Fan ZHANG Fangfang LIANG Tao WANG Limin XIAO Xiang WANG Hebei Agricultural University Beijing Information Science and Technology University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Power is an issue that must be considered in the design of logic circuits. Power optimization is a combinatorial optimization problem, since it is necessary to search for a logical expression that consumes the least amount of power from a large number of Reed-Muller(RM) logical expressions. The existing approach for optimizing the power of multi-output mixed polarity RM(MPRM) logic circuits suffer from poor optimization results. To solve this problem, a whale optimization algorithm with two-populations strategy and mutation strategy(TMWOA) is proposed in this paper. The two-populations strategy speeds up the convergence of the algorithm by exchanging information about the two-populations. The mutation strategy enhances the ability of the algorithm to jump out of the local optimal solutions by using the information of the current optimal solution. Based on the TMWOA, we propose a multi-output MPRM logic circuits power optimization approach(TMMPOA). Experiments based on the benchmark circuits of the Microelectronics Center of North Carolina(MCNC) validate the effectiveness and superiority of the proposed TMMPOA.

关键词： Multi-output mixed polarity Reed-Muller Power optimization Combinatorial optimization problem whale optimization algorithm Two-populations strategy Mutation strategy

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Application of Improved whale optimization algorithm in Parameter Identification of Hydraulic Turbine at No-Load

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第12期45卷 9913-9924页

作者： Tian, Tian Zhao, Wei Zhen, Wenxi Liu, Changyu Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China State Grid Gansu Elect Power Res Inst Lanzhou 730070 Gansu Peoples R China

The accuracy of hydraulic turbine model has a direct influence on controller design and system stability. This paper presents an improved whale optimization algorithm (IWOA) and its application in parameter identification of hydraulic turbine at no-load. In IWOA, two strategies including the increase in global exploration probability and combination of immune operator are introduced to avoid local optimum. Besides, in the identification process, the adaptive modification method is developed to solve the estimated parameter range uncertainty problem. Finally, in the example of Unit 4 in Huanglongtan Hydropower Plant, China, the results of different methods are compared. Considering the indicators of cost, iteration and total computation time, the results show that IWOA has faster convergence and higher precision than WOA.

关键词： Hydraulic turbine governing system Hydroelectric generating unit No-load Parameter identification whale optimization algorithm Measured data

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A direct method for solving calculus of variations problems using the whale optimization algorithm

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EVOLUTIONARY INTELLIGENCE 2019年第4期12卷 677-688页

作者： Mehne, Seyed Hamed Hashemi Mirjalili, Seyedali Aerosp Res Inst Tehran 14665834 Iran Griffith Univ Inst Integrated & Intelligent Syst Nathan Campus Brisbane Qld 4111 Australia

A numerical algorithm for solving problems of calculus of variations is proposed and analyzed in the present paper. The method is based on direct minimizing the functional in its discrete form with finite dimension. To solve the resulting optimization problem , the recently proposed whale optimization algorithms is used and adopted. The method proposed in this work is capable of solving constrained and unconstrained problems with fixed or free endpoint conditions. Numerical examples are given to check the validity and accuracy of the proposed method in practice. The results show the superior accuracy and efficiency of the proposed technique as compared to other numerical methods.

关键词： Calculus of variations whale optimization algorithm Numerical solution Finite difference

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