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Influence of Wind Energy Source on Congestion Management in Power System Transmission Network: a Novel Modified whale optimization Approach

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2022年第4期6卷 943-959页

作者： Paul, Kaushik Shekher, Vineet Kumar, Naveen Kumar, Vivek Kumar, Manoj BIT Sindri Dept Elect Engn Dhanbad 828123 Bihar India SRM Inst Sci & Technol Dept EEE Modinagar 201204 Uttar Pradesh India BRCM Coll Engn & Technol Dept EE Bhiwani 127028 Haryana India Gaya Coll Engn Dept Elect & Elect Engn Khizarsarai 823003 Bihar India

This manuscript proposes a Modified whale optimization algorithm for power system congestion cost problem based on the optimal real power rescheduling with integration of wind farm. Bus sensitivity factor and wind availability factor are utilized for the wind farm integration in the framework. The bus sensitivity factor signifies the most sensitive bus for the integration of the wind that will impact the power flow in the congested lines. The wind availability factor determines the optimal geographic location of wind availability factor based on the wind speed and availability of space. Wind Farm Position Factor has been developed for the optimal wind farm placement considering the combined weightage of bus sensitivity factor signifies and wind availability factor. The Modified whale optimization algorithm has been developed with the incorporation of two correction factors at the stages of exploration and exploitation of whale optimization algorithm to overcome its tendency of confining itself into local optima region with premature convergence. The performance of Modified whale optimization algorithm has been assessed based on the standard benchmark functions. IEEE-30 bus system is considered to evaluate the potency of the proposed Modified whale optimization algorithm for the transmission congestion management problem. The results opted with Modified whale optimization algorithm based on the congestion cost, bus voltage magnitudes, system losses, and computational time are found to be superior when contrasted with other optimization techniques.

关键词： Wind energy Cost minimization optimization techniques Renewable energy sources whale optimization algorithm

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Logistics Path Planning Method using NSGA-II algorithm and BP Neural Network in the Era of Logistics 4.0

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第5期15卷 163-173页

作者： Li, Liuqing Huanghuai Univ Dept Econ & Management Zhumadian 463000 Peoples R China

The distribution of fresh food is affected by its perishable characteristics, and compared with ordinary logistics distribution, the distribution path needs to be very reasonably planned. However, the complexity of the actual road network and the time variation of traffic conditions are not considered in the existing food logistics planning methods. In order to solve this problem, this study takes road section travel prediction as the starting point, and uses the non-dominant ranking genetic algorithm II and the backpropagation network to construct a new logistics path planning model. Firstly, the road condition information detected by the retainer detection and the floating vehicle technology is integrated, and the travel time prediction is input into the backpropagation network model. In order to make the prediction model perform better, it is improved using a whale optimization algorithm. Then, according to the prediction results, the non-dominant ranking genetic algorithm II is used for distribution route planning. Through experimental analysis, the average distribution cost of method designed by this study was 9476 yuan, and the average carbon emission was 2871kg. Compared with the other three algorithms, the distribution cost was more than 15% lower, and the carbon emission was more than 12.5% lower. The planning method designed by the institute can achieve more reasonable, low-cost, and environmentally friendly logistics and distribution, and bring more satisfactory services to the lives of urban residents.

关键词： whale optimization algorithm non-dominant ordering genetic algorithm backpropagation network logistics and distribution path planning

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Sliding mode control architecture for stabilization and tracking control of planar inverted pendulum

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2024年第3期12卷 774-784页

作者： Kumar, Satyendra Ajmeri, Moina Natl Inst Technol Patna Patna 800005 India

In this paper, a sliding mode control architecture consisting of four control blocks is proposed for the stabilization and control of a planar inverted pendulum. Optimal values of the controller constants are obtained using whale optimization algorithm. Different shapes of tracking path like circular, elliptical, infinity-shaped, etc., are considered to verify the tracking capability of the suggested method. Stability and robustness of the suggested control system are analyzed using Lyapunov stability criteria. Through various simulations, it is found that the proposed control strategy yields significantly improved tracking performance and enhanced robustness toward process parameter perturbations and load disturbances compared to conventional PID controller.

关键词： Planar inverted pendulum Robust control Sliding mode control whale optimization algorithm

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Selective ensemble of doubly weighted fuzzy extreme learning machine for tumor classification

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2024年第2期13卷 85-99页

作者： Wang, Yang Liaoning Petrochem Univ Sch Artificial Intelligence & Software Fushun 113001 Peoples R China

Malignant epithelial cell tumor also known as cancer is a deadly disease requiring a very costly and complex treatment. Early and accurate diagnosis of tumor plays an important role in reducing the mortality rate. With the rapid development of gene chip technology, gene expression data based tumor classification is helpful for accurate decision-making and has achieved great attention of researchers. Due to gene expression data having the properties of multi-class imbalance, high noise and high-dimensional small samples, in this paper, selective ensemble of doubly weighted fuzzy extreme learning machine (SEN-DWFELM) is presented for tumor classification. In view of good generalization performance of extreme learning machine (ELM), feature weighted fuzzy membership is embedded in ELM for eliminating classification error from noise samples. It considers the influence of feature importance on classification to acquire more accurate fuzzy membership. Simultaneously, by removing features with smaller weights it reduces the dimensionality of samples to improve training efficiency. Considering imbalanced learning, the weighted scheme is also introduced to enhance the effect of minority class samples on classification. Furthermore, doubly weighted fuzzy extreme learning machine (DWFELM) based selective ensemble algorithm is proposed to make classification performance more robust. Partial-based DWFELMs are selected using binary version of an improved whale optimization algorithm, and the selected base DWFELMs are integrated by majority voting. Finally, the proposed SEN-DWFELM is compared with conventional ensemble methods and variants of SEN-DWFELM on various gene expression data. Experimental results show that SEN-DWFELM remarkably outperforms other competitors in accordance with classification performance and can effectively deal with tumor diagnosis problems.

关键词： Gene expression data Feature weighted Fuzzy extreme learning machine Selective ensemble whale optimization algorithm

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A Novel Window Analysis and Its Application to Evaluating High-Frequency Trading Strategies

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COMPUTATIONAL ECONOMICS 2025年第2期65卷 795-818页

作者： Che-Ngoc, Ha Nguyen-Ngoc, Thach Nguyen-Trang, Thao Ton Duc Thang Univ Fac Math Stat Ho Chi Minh City Vietnam Ho Chi Minh Univ Banking 39 Ham Nghi St Dist 1 Ho Chi Minh City 70000 Vietnam Van Lang Univ Inst Computat Sci & Artificial Intelligence Lab Appl & Ind Math Ho Chi Minh City Vietnam Van Lang Univ Fac Fundamental Sci Ho Chi Minh City Vietnam

In order to examine the efficiency of decision-making units (DMUs) over time, the Window Data Envelopment Analysis (WDEA) is frequently applied. Since the WDEA considers a DMU at a period to be a distinct DMU, this method has several limitations, including a high computational cost and a lack of knowledge regarding the consistency of a DMU during a window. This study proposes a novel window analysis in which the information of a DMU during the window is linked, utilizing the new notions of "linked DMU" and "linked variable". Consequently, an inconsistent DMU in a window would not be eligible for a high efficiency score, despite the fact that it might be the most efficient at certain times. To approximate the globally optimal result, the whale optimization algorithm, one of the state-of-the-art meta-heuristics, is used. This ensures that the optimal solution is not trapped in local extremes, as is the case with linear programming methods. Furthermore, the proposed method is applied to evaluating the effectiveness of foreign exchange investment strategies as well as the effectiveness of companies in the utility industry, listed in the Ho Chi Minh City Stock Exchange. To our knowledge, this is the first time a WDEA-based method has been utilized in those fields. The results show that the new window analysis can identify effective and stable trading strategies/companies over time.

关键词： Data envelopment analysis Window analysis whale optimization algorithm Efficiency score Trading strategies

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Hybrid Solar and Wind Power Prediction Based on Optimal Decomposition and WOA optimization BiLSTM

Hybrid Solar and Wind Power Prediction Based on Optimal Deco...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Gao, Qi Zhang, Yunlong Tiangong University Tianjin China

ISBN: (纸本)9798350309614

Wind and solar energy are clean renewable energy sources, and when widely used renewable energy integration technology is connected to the grid for power generation, accurate and reliable wind speed and photovoltaic prediction is particularly important in power system regulation. As artificial intelligence technology develops, especially deep learning, more and more models are used in wind power and photovoltaic prediction due to their superiority in dealing with complex nonlinear problems. In this paper, a bidirectional long and short term attention model optimized by whale optimization algorithm (WOA) based on optimal modal decomposition is presented to improving prediction accuracy. In this model, wind and light variables timing feature is extracted by optimal variational mode method to obtain directly related features, and long-term prediction is made by combining WOA-optimized BiLSTM with attention mechanism. By comparing the prediction results of this model with other mixed models and the results of three evaluation indexes, the experiment shows that this model is superior to other models. © 2023 IEEE.

关键词： BiLSTM Power prediction Solor whale optimization algorithm Wind

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Simultaneous sensor and relay nodes deployment for Smart Car Park surveillance

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EVOLUTIONARY INTELLIGENCE 2024年第3期17卷 1587-1610页

作者： Benghelima, Slimane Charafeddine Ould Khaoua, Mohamed Benzerbadj, Ali Baala, Oumaya Univ Blida Dept Comp Sci LRDSI Route Soumaa 1 Route Soumaa Blida Algeria Univ Ain Temouchent Dept Math & Comp Sci Ain Temouchent Algeria Univ Oran 1 Ahmed Ben Bella RIIR Lab Oran Algeria UTBM Inst FEMTO ST CNRS F-90010 Belfort France

This study aims to optimize the deployment of wireless sensor networks for Smart Car Parks surveillance applications. Our proposed approach involves simultaneously deploying Sensor Nodes (SNs) and Relay Nodes, which we argue is more efficient than existing sequential placement methods. Our approach identifies optimal positions for both types of nodes to minimize their numbers and the network diameter while ensuring the coverage of each target point by at least K SNs and network connectivity constraints. To accomplish this, we have developed a Multi-Objective Linear Programming model (MOLP) to solve small instances of the deployment problem. Still, for large instances, we have introduced a novel algorithm called the Greedy Chaos whale optimization meta-heuristic (GCWOA) which is mainly based on: a Greedy algorithm to generate initial random nodes positions while satisfying the problem's constraints and a chaos local search (CLS) technique using chaos maps to decide whether to include a given node in the final solution. The solution population was divided into two sub-populations, with the first undergoing CLS and the second undergoing the whale optimization algorithm (WOA) to optimize the solution further. We have compared the performance of our proposed GCWOA with that of MOLP, and well-known existing meta-heuristics, including WOA, Genetic algorithm (GA), and Particle Swarm optimization (PSO). Our results reveal that the average fitness of the GCWOA is close to optimal and lower by 23.45%, 28.75%, and 26.32% than basic WOA, GA, and PSO, respectively. The GCWOA exhibits faster convergence and a lower running time than the basic WOA, GA, and PSO.

关键词： Internet of Things Smart Car Park surveillance Wireless sensor networks Simultaneous deployment Chaos local search whale optimization algorithm

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Online Strainer Based Scheduling (OSBS) for Improving Energy Efficiency in FiWi Access Networks

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期131卷 1249-1278页

作者： Mishra, Vijendra Upadhyay, Raksha Bhatt, Uma Rathore Kumar, Abhay Devi Ahilya Univ Inst Engn & Technol Dept Elect & Telecommun Khandwa Rd Indore India Devi Ahilya Univ Sch Elect Takshila ParisarKhandwa Rd Indore India

Integration of wireless frontend with optical backend leads to the fiber wireless access network, which provides high bandwidth and flexibility. Energy efficiency in such networks is very crucial and can be achieved by implementing multiplexing, scheduling and optimized components placement. Our prime objective is to improve energy efficiency. Architecture has wavelength agile and radio agile multiplexing at backend and frontend respectively. Firstly, we optimize optical network units using whale optimization and secondly, a novel online scheduling is implemented to obtain better energy efficiency with controlled delay. Proposed methodology shows better energy efficiency and delay performance compare to existing approaches.

关键词： whale optimization algorithm Online scheduling Fiber wireless access networks (FiWi) Energy efficiency Wavelength agile

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A hybrid stacking framework optimized method for TBM performance prediction

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2023年第1期82卷 1-16页

作者： Song, Kanglei Yang, Haiqing Wang, Zhihui Chongqing Univ Sch Civil Engn State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400045 Peoples R China Natl Joint Engn Res Ctr Geohazards Prevent Reservo Chongqing 400045 Peoples R China

The tunnel boring machine (TBM) performance directly affects the construction progress. The existing methods are difficult to improve and optimize the TBM performance. A new hybrid intelligent model named stacking framework optimized by whale optimization algorithm (Stacking-WOA) was developed to predict TBM penetration rate (PR) to overcome this problem. For this purpose, taking the tunnel of Shenzhen Metro Line 8 as an example, the most relevant parameters of TBM performance were collected and measured. In addition, three single models, including light gradient boosting machine, random forests, and extreme learning machine, were proposed to predict TBM PR and compared with the Stacking-WOA model. Moreover, to compare the results of these models and identify the best predictive models, several performance indices, such as the correlation coefficient (R), root mean square error, mean absolute percentage error, and a10-index, were computed to identify the best predictive models. The results show that the Stacking-WOA model is the most accurate in predicting TBM PR, and the stacking model outperforms the single algorithm model and has stronger learning and generalization ability for a small number of samples. Therefore, the stacking WOA model can accurately predict TBM PR and help guide and optimize tunneling construction projects in similar geological conditions.

关键词： Tunnel boring machine Penetration rate whale optimization algorithm Stacking framework

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Safety management system of new energy vehicle power battery based on improved LSTM

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Energy Informatics 2024年第1期7卷 101页

作者： Zhao, Kun Bai, Hao College of Automotive and Electromechanical Engineering Xinyang Vocational and Technical College Xinyang464000 China Marine Engineering College Dalian Maritime University Dalian116026 China

With the development of sustainable economy, new energy materials are widely used in various industries, and many cars also adopt new energy power batteries as power sources. However, it is currently not possible to accurately diagnose faults in power batteries, which results in the safety of power batteries not being guaranteed. To address this issue, this study utilizes the whale optimization algorithm to improve the Long Short-Term Memory algorithm and constructs a fault diagnosis model based on the improved algorithm. The purpose of using this model for fault diagnosis of power batteries is to strengthen the safety management of batteries. This study first conducted experiments on the improved algorithm and obtained an accuracy of 95.3%. The simulation results of the fault diagnosis model showed that the diagnosis time was only 1.2s. The analysis of the power battery showed that after using this model, the safety performance has been improved by 90.1%, while the maintenance cost has been reduced to 20.3% of the original. The above results verify that the fault diagnosis model based on the improved algorithm can accurately diagnose faults in power batteries, thereby improving the safety of power batteries. © The Author(s) 2024.

关键词： Fault diagnosis Long short-term memory Power battery Safety management whale optimization algorithm

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