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improved pelican optimization algorithm for solving load dispatch problems

ENERGY 2024年 289卷

作者： Seyedgarmroudi, Seyeddavoud Kayakutlu, Gulgun Kayalica, M. Ozgur Colak, Uner Istanbul Tech Univ Energy Inst Ayazaga Maslak TR-34469 Maslak Turkiye Istanbul Tech Univ Fac Management TR-34367 Besiktas Istanbul Turkiye

The pelican optimization algorithm (POA) is a newly developed algorithm inspired by the hunting behavior of pelicans. Despite its fast convergence rate, it suffers from premature convergence, the imbalance between exploration and exploitation, and lack of population diversity. In this work, an improved POA is proposed to attenuate these shortcomings. IPOA benefits from three motion strategies and predefined knowledge-sharing factors that better describe the stochastic hunting behavior of pelicans, as well as a modified dimensionlearning-based hunting (DHL) behavior to retain diversity. To test the effectiveness of these improvements, it was used to solve 23 benchmark functions, including unimodal, multimodal, and 6 composite functions (CEC 2017). To evaluate performance, it was applied to solve economic and combined economic emission load dispatch problems that play a critical role in real-world power system planning and operation while considering environmental impacts. This experiment includes 6, 10, 11, 40, 140, 160, and 320 generating units with nonconvex and non-smooth objective functions. The comparison is performed for benchmark functions and the optimal dispatch problems. The results confirm the competitive and almost superior performance of the IPOA in several cases, which proves the applicability and efficiency of the proposed approach in solving real-world problems.

关键词： Metaheuristic Swarm intelligence optimization Combined economic emission load dispatch improved pelican optimization algorithm

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Capacity optimization of Independent Microgrid with Electric Vehicles Based on improved pelican optimization algorithm

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ENERGIES 2023年第6期16卷 2539-2539页

作者： Li, Jiyong Chen, Ran Liu, Chengye Xu, Xiaoshuai Wang, Yasai Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Levy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.

关键词： uncertainty electric vehicles independent microgrid capacity optimization improved pelican optimization algorithm

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Capacity optimization Configuration of Hybrid Energy Storage Systems for Wind Farms Based on improved k-means and Two-Stage Decomposition

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ENERGIES 2025年第4期18卷 795-795页

作者： Zhang, Xi Kang, Longyun Wang, Xuemei Liu, Yangbo Huang, Sheng South China Univ Technol Sch Elect Power Guangzhou 510641 Peoples R China Longdong Univ Coll New Energy Qingyang 745000 Peoples R China

To address the issue of excessive grid-connected power fluctuations in wind farms, this paper proposes a capacity optimization method for a hybrid energy storage system (HESS) based on wind power two-stage decomposition. First, considering the susceptibility of traditional k-means results to initial cluster center positions, the k-means++ algorithm was used to cluster the annual wind power, with the optimal number of clusters determined by silhouette coefficient and Davies-Bouldin Index. The overall characteristics of each cluster and the cumulative fluctuations were considered to determine typical daily data. Subsequently, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) was used to decompose the original wind power data for typical days, yielding both the grid-connected power and the HESS power. To leverage the advantages of power-type and energy-type storage while avoiding mode aliasing, the improved pelican optimization algorithm-variational mode decomposition (IPOA-VMD) was applied to decompose the HESS power, enabling accurate distribution of power for different storage types. Finally, a capacity optimization model for a HESS composed of lithium batteries and supercapacitors was developed. Case studies showed that the two-stage decomposition strategy proposed in this paper could effectively reduce grid-connected power fluctuations, better utilize the advantages of different energy storage types, and reduce HESS costs.

关键词： power fluctuations hybrid energy storage system k-means plus plus improved complete ensemble empirical mode decomposition with adaptive noise variational mode decomposition improved pelican optimization algorithm

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Prediction of carbon content and temperature at the end of converter steelmaking based on Just-in-time learning

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METALLURGICAL RESEARCH & TECHNOLOGY 2025年第2期122卷

作者： Hou, Zhichao Li, Ailian Song, Yijing Xie, Shaofeng Inner Mongolia Univ Sci & Technol Sch Automat & Elect Engn Baotou 014000 Nei Mongol Peoples R China Guang Dong Peizheng Coll Sch Management Guangzhou 510000 Guangdong Peoples R China Inner Mongolia Univ Sci & Technol Infrastruct Dept Baotou 014000 Nei Mongol Peoples R China

Accurate carbon content and temperature prediction at the endpoint of converter steelmaking can provide essential reference values for the control of the endpoint. In order to achieve accurate prediction, a kind of mutual information (MI) weighted Just-in-time learning (JITL) based on Kmeans clustering was proposed, aiming at the characteristics of high dimensionality, nonlinearity, and high volatility of converter steelmaking process data. The JITL modeling method predicts the carbon content and temperature at the end of converter steelmaking. Firstly, the improved pelican optimization algorithm (MPOA) was used for feature selection, reduced data dimension, and critical factors affecting carbon content and temperature at the endpoint were screened out as input to the model. Secondly, the input sample data was clustered to reduce the impact of data fluctuations on the model. Finally, by weighting the mutual information of the prediction and training samples, the local algorithm learning set is constructed according to the degree of contribution of the samples. It is used for the local model training of the hybrid kernel Extreme Learning Machine (HKELM) hyperparameters optimized by MPOA. Finally, the carbon content and temperature are predicted. The experimental results show that the hit rate of carbon content is 95% at +/- 0.03%, and the hit rate of temperature is 97% at +/- 15 degrees C.

关键词： improved pelican optimization algorithm hybrid kernel extreme learning machine Kmeans clustering mutual information weighting just-in-time learning

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Rapid Detection of Formaldehyde Emission from Wood-Based Panels Based on the IPOA-XGBoost 5th

Rapid Detection of Formaldehyde Emission from Wood-Based Pan...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Wang, Yinuo Zheng, Huanqi Wu, Qiang Zhou, Yucheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Shandong Peoples R China Shandong Jianzhu Univ Sch Architecture & Urban Planning Jinan 250101 Shandong Peoples R China Natl Ctr Qual Inspect & Test Decorat Mat Jinan 250102 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Aiming at the complexity of the process of analyzing formaldehyde composition by physicochemical methods in the process of formaldehyde emission detection by the full-scale chamber method, and the problem of bias in the accuracy of sensor method, the IPOA-XGBoost formaldehyde emission modified deviation model for a class of panels is proposed for the fast and accurate detection of formaldehyde emission from wood-based panels. The model utilizes data obtained from the electrochemical sensor as input. The output is the formaldehyde emission measured by the full-scale chamber method. The model is fitted using the extreme gradient boosting (XGBoost). To address the weak optimization ability in the pelican optimization algorithm (POA), random opposition-based learning is employed to establish the initial population. Additionally, nonlinear weighting factors and a sparrow alert mechanism perturbation strategy are introduced to enhance the ability of global optimization and escape local optimal solution. The improved POA (IPOA) is used to optimize the key parameters of XGBoost, and then the modified deviation model of formaldehyde emission of the IPOA-XGBoost wood-based panel is constructed, and compared with four machine learning models. Experimental results indicate that the proposed model achieves a coefficient of determination of 0.9701 and a root mean square error of 2.7648e-03. Furthermore, it exhibits lower prediction errors compared to other models tested herein. This provides an effective and reliable solution for rapid detection of formaldehyde emission in wood-based panels.

关键词： The full-scale chamber improved pelican optimization algorithm Extreme gradient boosting Formaldehyde emission detection

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Free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2024年 108卷

作者： Chen, Yugeng Zhong, Rui Wang, Qingshan Chen, Liming Qin, Bin Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China Cent South Univ State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Cent South Univ Sch Traff & Transportat Engn Key Lab Traff Safety Track Minist Educ Changsha 410075 Peoples R China Cent South Univ Joint Int Res Lab Key Technol Rail Traff Safety Changsha 410075 Peoples R China Cent South Univ Natl & Local Joint Engn Res Ctr Safety Technol Rai Changsha 410075 Peoples R China

In this study, the free vibration analysis and multi-objective robust optimization of three-dimensional pyramidal truss core sandwich plates with interval uncertain parameters are fulfilled. The numerical model for free vibration of the plate is derived by combining the three-dimensional elasticity theory and Rayleigh-Ritz method, and the validity of the model is illustrated by numerical results. On this basis, considering various uncertainties within the plate, a new uncertainty-propagation analysis method is constructed by integrating the numerical model, interval-analysis model, kriging model and optimization. The one-dimensional and multi-source uncertainty-propagation analysis of the fundamental frequency is finished using this method, and the accuracy is proved by comparing with Monte Carlo simulation results. Meanwhile, to minimize the effects of uncertainties on the performance of plates at the design stage, an objective multi-objective robust optimization model with the objectives of maximizing the fundamental frequency and minimizing the robustness factor is established. Finally, an improved pelican optimization algorithm is proposed by introducing the improved opposition-based learning strategy, tracking strategy with step control and convergence strategy. And the Pareto front and corresponding design schemes applicable to different working environments are obtained without repeating the optimization.

关键词： Three-dimensional pyramidal truss core sandwich plates Interval uncertainty Free vibration Multi-objective robust optimization improved pelican optimization algorithm

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Transformer Fault Diagnosis Based on improved POA Optimized HKELM 3

Transformer Fault Diagnosis Based on Improved POA Optimized ...

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3rd International Conference on Electrical Engineering and Control Science, IC2ECS 2023

作者： Zhuang, Hongda Xie, Guomin Liaoning Technical University Faculty of Electrical and Control Engineering Huludao China

ISBN: (纸本)9798350382426

In order to improve the accuracy of the transformer fault diagnosis, this paper presents an improved pelican optimization algorithm (IPOA) optimized hybrid kernel extreme learning machine (HKELM). First, Principal Component Analysis (PCA) is used to extract features from 20 dimensional transformer fault feature data to eliminate the correlation between variables while reducing the dimension;Secondly, the population was initialized using the Good-point set, Improve the accuracy and speed of the algorithm convergence;Introducing reverse differential evolution, adaptive t distribution variation, Expand the search range of the algorithm for the optimal solution, Searching for the region surrounding the superior solution, Ability to optimize the algorithm for fault identification and convergence speed, Prevent the algorithm from falling into the local optima;Finally, the IPOA algorithm was used to optimize the core parameters of HKELM, and the fault diagnosis accuracy of IPOA-HKELM transformer model reached 96%. Compared with the diagnosis accuracy of POA, GWO and PSO algorithm, the diagnosis accuracy was improved by 7%, 10% and 11%, respectively, which proved that IPOA-HKELM can effectively improve the fault diagnosis performance. © 2023 IEEE.

关键词： fault diagnosis hybrid kernel extreme learning machine improved pelican optimization algorithm transformer

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Heuristic optimization-Assisted Dilated Convolution Neural Network With Gated Recurrent Unit for Channel Estimation in NOMA-OFDM System

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IEEE ACCESS 2024年 12卷 184456-184476页

作者： Kondepogu, Vijayakumar Bhattacharyya, Budhaditya Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 India

The "Non-Orthogonal Multiple Access (NOMA)" strategies has been recently identified as a successful way to increase spectrum effectiveness and reliability of the system. NOMA enables numerous users to share identical blocks of resources with varying power distribution factors. Thus, everyone can utilize equal resource structures, resulting in improved spectral effectiveness. "Orthogonal Frequency-Division Multiplexing (OFDM)" represents a well-known multi-carrier broadcast technique that provides a high data prevalence, excellent spectral effectiveness, and resilience against network band selection for present and future wideband wireless connections. However, channel estimation remains one of the most important challenges in OFDM based communication. Pilot-aided estimation of channels is less difficult and performs better than blind channel estimation;therefore, it is commonly employed in real-world systems. However, inadequate Successive Interference Cancellation (SIC) may have an impact on NOMA effectiveness. To help with channel estimates and signal identification in NOMA structures, deep learning approaches were developed. In this paper, we introduce a new approach called Adaptive Dilated Convolutional Neural Networks with Gated Recurrent Unit Layer (ADCNN-GRU). Initially, the required signals are collected from the standard data resource. The input signal is then subjected to the developed ADCNN-GRU approach, which is the combination of the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) with an additional dilated layer. Here the input signals are extracted and infer the signal at the receiver terminal. The loss functions in the model are optimized by using the improved pelican optimization algorithm (IPOA). The performance of the developed approach is determined by conducting the simulation experiment. The result showed that the developed approach outperformed than traditional models.

关键词： Channel estimation NOMA OFDM Estimation Wireless communication Deep learning Accuracy Training Convolutional neural networks optimization non-orthogonal multiple access orthogonal frequency-division multiplexing adaptive dilated convolutional neural network with gated recurrent unit improved pelican optimization algorithm

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A smart structural optimization method of magnetorheological damper for ultra-precision machine tool

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SMART MATERIALS AND STRUCTURES 2024年第5期33卷

作者： Wang, Qiyu Shen, Yurui Li, Weihua Liu, Xinhua Goldasz, Janusz Deng, Lei Krolczyk, Grzegorz Li, Zhixiong China Univ Min & Technol Sch Mech & Elect Engn Xuzhou Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia Cracow Univ Technol Fac Elect & Comp Engn Dept Elect Engn PL-31155 Krakow Poland Opole Univ Technol Fac Mech Engn Opole Poland

To address the problem of multi-source vibration in ultra-precision machine tools, a vibration reduction stand was designed by replacing passive damping components with magnetorheological dampers (MRDs). In this work, the structural parameters of MRDs were optimized using an improved pelican optimization algorithm (IPOA) to realize the maximum capability in reducing vibration. Firstly, the working principle of MRDs was explained, and the mathematical models of MRDs were established. Then, an IPOA based on singer chaotic mapping, nonlinear inertia weight factor, and Cauchy mutation strategy was proposed to enhance the global search capability and convergence efficiency of the algorithm. Subsequently, the IPOA was applied to optimize key structural parameters of MRDs, including output damping force, controllable damping range, response time, and power consumption. Finally, COMSOL Multiphysics software was used to verify the effectiveness of the proposed algorithm by comparing the magnetic induction intensity distribution of MRDs before and after optimization, as well as the variation of the four performance indexes under the different applied currents. After being optimized using the proposed IPOA, the MRDs can deliver a larger maximum damping force and a wider damping controllable range, with less power consumption and quick response, which could meet the requirement for vibration suppression of ultra-precision machine tools.

关键词： ultra-precision machine tool magnetorheological damper improved pelican optimization algorithm optimization of structural parameters

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