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chaotic whale optimization algorithm for optimal solution of combined heat and power economic dispatch problem incorporating wind

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RENEWABLE ENERGY FOCUS 2020年 35卷 56-71页

作者： Paul, Chandan Roy, Provas Kumar Mukherjee, V. Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Bihar India

In this article, a new practical approach is implemented to analyze combined heat and power economic dispatch (CHPED) and wind based CHPED (CHPEDW) problems with a chaotic based whale optimization algorithm (WOA). The conflict objectives of this problem are used to minimize fuel costs as well as emission. The proposedWOAworks on the searching behavior of humpback whales, which helps to satisfy the problem's objective functions. The chaotic nature has been integrated with WOA to enhance the convergence speed of the problem. The presence of valve point loading, prohibited operating zone, and uncertainty of wind has expanded the problem nonlinearity. It has been tested on two different nonlinear realistic power areas to judge the satisfactory results of the proposed algorithm. The superiority of the proposed algorithm is judged by comparing it with presently developed some other meta-heuristic optimization techniques.

关键词： chaotic whale optimization algorithm Combined heat and power economic dispatch whale optimization algorithm Wind based combined heat and power economic dispatch.

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Experimental verification of a hybrid control scheme with chaotic whale optimization algorithm for nonlinear gantry crane: A comparative study

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ISA TRANSACTIONS 2020年第0期98卷 418-433页

作者： Hamdy, Mohamed Shalaby, Raafat Sallam, Mostafa Menoufia Univ Fac Elect Engn Dept Ind Elect & Control Engn Menoufia 32952 Egypt Nile Univ Sch Engn & Appl Sci Mechatron Engn Dept Giza 12588 Egypt

This paper proposes an experimental verification of a hybrid partial feedback linearization (PFL) and deadbeat (DB) control scheme as in Hamdy et al. (2018) with chaotic whale optimization algorithm (CWOA) for a nonlinear gantry crane (GC) system. The PFL linearizes the nonlinear model to end up with a linear closed-loop system. The DB controller is utilized for the desirable accelerated response without any oscillation or undesirable effects on the internal dynamics stability. The CWOA is used to tune the controller parameters. A sliding-mode observer (SMO) is utilized to estimate the unmeasured states. Using this hybrid scheme, a better payload sway elimination can be obtained. Finally, the experimental results are presented to illustrate the efficiency and the effectiveness of the proposed scheme with a comparative study. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Nonlinear gantry crane Deadbeat control chaotic whale optimization algorithm Sliding-mode observer Partial feedback linearization

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A chaotic whale optimization algorithm (CWOA) based multiobjective conjoint spectrum utilization in a cognitive radio (CR) network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2023年第18期36卷 e5607-e5607页

作者： Swathi, Bankapalli Prakash, M. Sunil Krishna, Battula Tirumala JNTU Dept Elect & Commun Engn Kakinada Andhra Pradesh India Maharaj Vijayaram Gajapathi Raj Coll Engn A Dept Elect & Commun Engn Vizianagaram Andhra Pradesh India

In this manuscript, a novel energy detection-based chaotic whale optimization algorithm (ED-CWOA) is proposed to maximize the spectrum utilization, duration, and threshold in cognitive radio network (CRN). Here, the spectrum utilization is designed taking into account sensing duration and threshold in multi-objective function is formulated by considering its primary user (PU) protection restrictions and secondary user (SU) throughput. To get the global optimal solution, the CWOA and the multi-objective optimization problem are finally carried out on a single objective. The execution is performed on NS2 simulator. The simulation outcome demonstrates that the proposed ED-CWOA system has lower delay 33.878%, 26.206%;higher delivery ratio 35.536%, 22.65%;lower Fairness Index 46.66%, 58.332%;lower energy consumption 25.462%, 35.524%;and higher throughput 96.82%, 49.28% for different nodes analyzed with the existing methods, like energy detection-based bivariate Levy-stable bat algorithm (ED-BLSBA) and genetic algorithm-based energy detection (ED-GA), respectively. The simulation outcome indicates that the proposed system may be able to get the optimal global solutions efficiently and accurately.

关键词： chaotic whale optimization algorithm cognitive radio (CR) network detection threshold duration energy detection spectrum sensing spectrum utilization

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Fault diagnosis in electric motors using multi-mode time series and ensemble transformers network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-33页

作者： Xu, Bo Li, Huipeng Ding, Ruchun Zhou, Fengxing Huanggang Normal Univ Sch Phys & Elect Informat Huanggang 438000 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 438001 Peoples R China

Induction motors are essential in industrial production, and their fault diagnosis is vital for ensuring continuous and efficient equipment operation. Minimizing downtime losses and optimizing maintenance costs are key to maintaining smooth production and enhancing economic efficiency. This paper presents a novel diagnostic approach for diverse motor faults, integrating time series analysis, Transformer-based networks, and multi-modal data fusion. Firstly, multiple signals such as three-phase current, vibration, device sound, and ambient sound are collected to form a multi-modal dataset. Subsequently, a Transformer network for single time series classification is developed, and multiple instances are concatenated in parallel to create an ensemble Transformer network. The self-attention mechanism is then utilized to dynamically integrate features from different modal data for accurate motor fault identification. During network training, the chaotic WOA optimizes the ensemble Transformer network's hyper-parameters. Finally, the proposed method is trained and tested on a motor measurement multi-modal dataset. Experimental results show that it performs outstandingly on multi-modal datasets, attaining a high diagnostic accuracy of 99.10%. Compared with single-mode data and state-of-the-art methods, it demonstrates superior diagnostic accuracy and reliability.

关键词： Induction motor Fault diagnosis Ensemble transformers network chaotic whale optimization algorithm Multi-mode

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A load forecasting model based on support vector regression with whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第7期82卷 9939-9959页

作者： Lu, Yuting Wang, Gaocai Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Guangxi Univ Sch Comp Elect & Informat Nanning 530004 Peoples R China

Power load forecasting is an important part of smart grid, and its accuracy will directly affect the control and planning of power system operation. In the context of electricity market reform, real-time electricity prices affect users' electricity consumption patterns. A short-term load forecasting model based on support vector regression (SVR) with whale optimization algorithm (WOA) considering real-time electricity price is proposed in this paper. Meta-heuristics are very promising in optimizing the parameters of SVR, and the WOA algorithm is used to determine the appropriate combination of SVR's parameters to accurately establish a forecasting model. The initial value of the original WOA algorithm lacks ergodicity, and has defects such as easy to fall into local optimum and low convergence accuracy. Chaos mechanism and elite opposition-based learning strategy are introduced into WOA to balance the exploration and exploitation of the algorithm and improve the algorithm convergence speed. Numerical examples involving two power load datasets show that the proposed model can achieve better forecasting performance in comparison with other models, such as SVR, BPNN. At the same time, it proves that the forecasting accuracy with electricity price is higher than that without electricity price.

关键词： Short-term load forecasting Real-time electricity price Support vector regression chaotic whale optimization algorithm Elite opposition-based learning

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Integration of DG and Capacitor in Radial Distribution Networks Using an Efficient Hybrid optimization Method

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第11期48卷 1102-1110页

作者： Mahmoud, Ismail Kamel, Salah Abdel-Mawgoud, Hussein Nasrat, Loai Jurado, Francisco Aswan Univ Dept Elect Engn Fac Engn Aswan Egypt Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China Univ Jaen Dept Elect Engn 23700 EPS Linares Jaen Spain

Recently, integration of distributed generations (DGs) and shunt capacitors in radial distribution network are becoming popular to withstand the rapidly increasing in electricity demands. In this article, a hybrid method is proposed to obtain the best locations and sizes of capacitors and DGs in radial distribution network. This hybrid method is based on chaotic whale optimization algorithm (CWOA) and loss sensitivity factor (LSF). The proposed method is tested on IEEE 69-bus system to enhance the buses voltages, increase the distribution system capacity, and decrease the total power loss. The obtained results prove that the proposed method based on CWOA and LSF can be highly effective in determining the locations and sizes of capacitors and DGs in RDS compared with other optimization techniques.

关键词： distribution system DG capacitor LSF chaotic whale optimization algorithm

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Network dynamics of a periodically forced chemical system and its application for tuning PID controller with time-delay systems

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NONLINEAR DYNAMICS 2023年第14期111卷 13601-13617页

作者： Zhao, Xiu Wang, Xingyuan Su, Yining Unar, Salahuddin Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Liaoning Peoples R China

In this paper, network dynamics are investigated in a periodically forced chemical system. At the same time, the ring network and ring-star network based on the periodically forced chemical system are designed. The chaotic dynamics of the ring network and ring-star network are analyzed by using the Lyapunov exponent spectrum, bifurcation diagram and correlation function. We show that the coupling strength of ring network has an important influence on chaotic dynamics and synchronization. By comparing ten, eleven and 100 nodes, we find that the bifurcation path of the ring-star network is robust to the number of nodes, which is different from the ring network. In addition, the ring-star network in comparison with the ring network achieved chaotic complete synchronization among all nodes. Finally, we proposed a new chaotic whale optimization (CWO) algorithm using the randomness of the ring-star network. It is used to tune the parameters of the PID controller with large time-delay systems. The simulation results show that the proposed CWO algorithm presents better performance than other available algorithms in the literature.

关键词： chaotic dynamics Ring network Ring-star network Synchronization chaotic whale optimization algorithm

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A novel method for carbon emission forecasting based on Gompertz's law and fractional grey model: Evidence from American industrial sector

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RENEWABLE ENERGY 2022年第0期181卷 803-819页

作者： Gao, Mingyun Yang, Honglin Xiao, Qinzi Goh, Mark Hunan Univ Sch Business Adm Changsha 410082 Hunan Peoples R China Natl Univ Singapore NUS Business Sch Singapore 5117592 Singapore Natl Univ Singapore Logist Inst Asia Pacific Singapore 5117592 Singapore Univ Manitoba Asper Sch Business Winnipeg MB R3T 2N2 Canada

ABSTR A C T With the manufacturing reshoring to the US, increasing attention are focus on its energy consumption and environmental effects and accurate prediction of carbon emissions is vital to controlling growth from the source. Considering the slowing growth in carbon emissions with the Gompertz's law, this paper establishes a Gompertz differential equation. According to the differential information principle and fractional accumulation operator, this differential equation is transformed into a fractional accumulation grey Gompertz model. Furthermore, the chaotic whale optimization algorithm is used to optimize the order of accumulation generation and the grey background value in the proposed model. Then the Gompertz's datasets and six validation cases about carbon emissions are used to show that the proposed model demonstrates better accuracy in all cases and efficiency in the carbon emissions forecasting with several existing models. Three case studies indicate that the proposed model can fit the trend of American industrial carbon emissions better. The model results also reveal the recent policy changes have promoted the uptrend of the industrial and the total carbon emissions in the U.S. The future forecasting suggests that U.S. carbon emission is estimated to be 17.01% (in total emissions) or 17.89% (in industrial emission) percent below 2005 levels by 2025 under current policies, falling short of its commitment submitted to the United Nations Framework Convention on Climate Change. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Carbon emissions Grey model Gompertz differential equation Fractional accumulation chaotic whale optimization algorithm

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