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7th International Conference on Energy, Electrical and Power Engineering (CEEPE)

作者： Ma, Jiangong Li, Qing Li, Ming Turgun, Yaxar Di, Qiang State Grid Xinjiang Elect Power Res Inst New Power Syst Technol Res Ctr Urumqi Peoples R China Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

ISBN: (纸本)9798350375794;9798350375800

With the rapid growth of distributed photovoltaic scale, the impact of random fluctuation characteristics of its output on urban load is also increasing. Facing the load forecasting scenario of large-scale distributed PV access, this paper proposes a short-term load forecasting method considering the influence of distributed PV. The side load of the grid under photovoltaic access is the difference between the actual electricity load and photovoltaic output. Therefore, before constructing input data, this paper first adopts big data mining technology to analyze the characteristics of photovoltaic output and user side load and the correlation between them and their respective influencing factors. The influential factors with high correlation are selected as the input feature set of the load forecasting model through feature construction. Then, the LSTM neural network prediction model with self-attention mechanism is constructed to deeply explore the load sequence characteristics. grey wolf algorithm was used to optimize the prediction model, and the model with the best prediction effect was determined. The numerical results show that the proposed method can effectively improve the prediction accuracy of net load with distributed PV.

关键词： distributed photovoltaic correlation analysis Self-attention mechanism LSTM grey wolf optimization algorithm load forecasting

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ICP algorithm based on stochastic approach 44

ICP algorithm based on stochastic approach

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Conference Applications of Digital Image Processing XLIV

作者： Voronin, Sergei Makovetskii, Artyom Kober, Vitaly Voronin, Aleksei Chelyabinsk State Univ Dept Math 129 Br Kashirinykh Chelyabinsk 454001 Russia CICESE Dept Comp Sci Carretera Ensenada Tijuana 3918 Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

3D reconstruction has been widely applied in medical images, industrial inspection, self-driving cars, and indoor modeling. The 3D model is built by the steps of data collection, point cloud registration, surface reconstruction, and texture mapping. In the process of data collection, due to the limited visibility of the scanning system, the scanner needs to scan multiple angles and then splice the data to obtain a complete point cloud model. The point clouds from different angles must be merged into a unified coordinate system, which is known as point cloud registration. The result of point cloud registration can directly affect the accuracy of the point cloud model;thus, point cloud registration is a key step in the construction of the point cloud model. The ICP (Iterative Closest Points) algorithm is the most known technique of the point cloud registration. The variational ICP problem can be solved not only by deterministic but also by stochastic methods. One of them is grey wolf Optimizer (GWO) algorithm. Recently, GWO has been applied to rough point clouds alignment. In the proposed paper, we apply the GWO approach to the realization of the point-to-point ICP algorithms. Computer simulation results are presented to illustrate the performance of the proposed algorithm.

关键词： surface reconstruction point cloud registration iterative closest points (ICP) variational functionals orthogonal transformations grey wolf optimization algorithm

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Hybridized Intelligent Neural Network optimization Model for Forecasting Prices of Rubber in Malaysia

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Computer Systems Science & Engineering 2023年第11期47卷 1471-1491页

作者： Shehab Abdulhabib Alzaeemi Saratha Sathasivam Majid Khan bin Majahar Ali K.G.Tay Muraly Velavan Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn MalaysiaParit Raja86400Malaysia School of Mathematical Sciences Universiti Sains MalaysiaPenang11800Malaysia School of General&Foundation Studies AIMST UniversityBedong08100Malaysia

Rubber producers,consumers,traders,and those who are involved in the rubber industry face major risks of rubber price *** a result,decision-makers are required to make an accurate estimation of the price of *** paper aims to propose hybrid intelligent models,which can be utilized to forecast the price of rubber in Malaysia by employing monthly Malaysia’s rubber pricing data,spanning from January 2016 to March *** projected hybrid model consists of different algorithms with the symbolic Radial Basis Functions Neural Network k-Satisfiability Logic Mining(RBFNN-kSAT).These algorithms,including grey wolf optimization algorithm,Artificial Bee Colony algorithm,and Particle Swarm optimization algorithm were utilized in the forecasting data *** factors,which affect the monthly price of rubber,such as rubber production,total exports of rubber,total imports of rubber,stocks of rubber,currency exchange rate,and crude oil prices were also considered in the *** evaluate the results of the introduced model,a comparison has been conducted for each model to identify the most optimum model for forecasting the price of *** findings showed that GWO with RBFNN-kSAT represents the most accurate and efficient model compared with ABC with RBFNNkSAT and PSO with RBFNN-kSAT in forecasting the price of *** GWO with RBFNN-kSAT obtained the greatest average accuracy(92%),with a better correlation coefficient R=0.983871 than ABC with RBFNN-kSAT and PSO with ***,the empirical results of this study provided several directions for policymakers to make the right decision in terms of devising proper measures in the industry to address frequent price changes so that the Malaysian rubber industry maintains dominance in the international markets.

关键词： Rubber prices in Malaysia grey wolf optimization algorithm radial basis functions neural network k-satisfiability commodity prices

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Tool wear prediction method based on KPCA and GWO-LS-SVM 3

Tool wear prediction method based on KPCA and GWO-LS-SVM

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2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023

作者： Liu, Hongjun Hu, Yiwei Wang, Yiming Shenyang Aerospace University Shenyang China General Technology Group Machine Tool Engineering Research Institute Co. LTD. Shenyang Branch Shenyang China

ISBN: (纸本)9798350341546

To improve the real-time and accuracy of detecting tool wear status, a new tool wear prediction method based on Kernel Principal Component Analysis (KPCA) and grey wolf Optimized Least Squares Support Vector Machine (GWO-LS-SVM) was proposed. This method uses cutting force signals to extract multiple features correlated with tool wear from time domain, frequency domain, and wavelet domain, and performs dimensionality reduction processing through kernel principal component analysis, then uses the gray wolf to optimize the LS-SVM to achieve prediction of tool wear. Through experimental comparison and verification, it is found that this method can accurately predict the value of tool wear, and has good real-time performance. © 2023 IEEE.

关键词： grey wolf optimization algorithm kernel principal component analysis least squares support vector machine tool wear prediction

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Wind Power Prediction Based on Gray wolf algorithm and Long Short-Term Memory Neural Networks 3

Wind Power Prediction Based on Gray Wolf Algorithm and Long ...

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

作者： Ai, Ning Dong, Yutao Chen, Yangyang Xi'An Polytechnic University Electronic Information School Xi'An China

ISBN: (纸本)9798350382426

The prediction of wind power generation is of great significance for improving the reliability of power system and promoting the development of renewable energy. In order to improve the accuracy of wind forecasting, a wind forecasting model based on the grey wolf optimisation algorithm has been developed to optimise long-term and short-term memory networks. Firstly, the principle of long short-term memory network and grey wolf optimization algorithm is introduced. Then, a GWO optimized LSTM wind power prediction model is established, which mainly includes data preprocessing, LSTM hyperparameter optimization and model training. Finally, the LSTM model with the best hyperparameters is used to predict the wind power generation. The case analysis results show that compared with single LSTM, BP, RBF and SVM methods, the GWO-LSTM can more accurately match future trends in wind power generation, improving the accuracy of wind power forecasting. © 2023 IEEE.

关键词： grey wolf optimization algorithm hyperparameter optimization long short term memory network wind power prediction

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An Application of grey wolf Optimizer for Optimal Power Flow of Wind Integrated Power Systems 18

An Application of Grey Wolf Optimizer for Optimal Power Flow...

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18th International Scientific Conference on Electric Power Engineering (EPE)

作者： Siavash, Majid Pfeifer, Christoph Rahiminejad, Abolfazl Vahidi, Behrooz Univ Nat Recourse & Life Sci Dept Mat Sci & Proc Engn Vienna Austria Esfarayen Univ Technol Dept Elect & Comp Sci Esfarayen Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781509064069

In this paper, the problem of optimal power flow (OPF) is solved in a system integrated with wind farms with the aim of reducing the cost of power production and the reduction of power grid loss by applying the grey wolf optimization (GWO) algorithm. The variable nature of wind farm output is modeled using two additional cost components corresponding to the states of under estimation and over estimation, where the available power is higher and lower than the scheduled output, respectively. On one side, in the case where there is lower power regards to the planned power, a penalty is added to the cost function. On the other side, if the produced power would be more than the planned power, an additional cost would be added to the cost function because of not buying the overall power of the wind farms. A recently introduced optimization method known as grey wolf optimization algorithm is employed in this article. The problem of OPF based on proposed approach has been applied on a modified version of IEEE 30-bus test system. The results of this study are compared with the results of Genetic algorithm (GA). The results show the superiority of the proposed method, both in the convergence speed as well as the final result comparing to other method.

关键词： Optimal power flow wind farm grey wolf optimization algorithm genetic algorithm

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Mechanical Fault Diagnosis of Transformer Core Based on VMD and GWO-HKELM 3

Mechanical Fault Diagnosis of Transformer Core Based on VMD ...

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

作者： Cheng, Long Ma, Hongzhong Xiao, Yusong Chen, Yuelin College of Energy and Electrical Engineering Hohai University Nanjing211100 China

ISBN: (纸本)9798350382426

Aiming at the problem that it is difficult to accurately identify the mechanical fault of the transformer core, a method of transformer core mechanical fault diagnosis based on variational mode decomposition (VMD)-multi-scale permutation entropy (MPE) and a gray wolf optimization algorithm to optimize the parameters of hybrid kernel extreme learning machine (GWO-HKELM) is proposed. Firstly, VMD is used to decompose the transformer vibration signal and select multiple IMF components that are highly correlated with the original vibration signal. Secondly, we calculate the MPE values of the IMF components and use them to construct a feature dataset. Furthermore, GWO is used to optimize the key kernel parameters of HKELM and establish a GWO-HKELM diagnostic model. Then, the feature dataset is imported into the GWO-HKELM diagnostic model for recognition. Finally, the effectiveness and superiority of the suggested method are verified through experimental data and comparison with other methods. The experimental results show that the suggested method can effectively identify different types and degrees of mechanical faults caused by transformer core looseness, with a diagnostic accuracy of over 98.0%. It can provide theoretical guidance for the formulation of on-site maintenance strategies for transformers. © 2023 IEEE.

关键词： Fault diagnosis grey wolf optimization algorithm Hybrid kernel Extreme learning machine Iron core Power transformer

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Towards Designing Durable Sculptural Elements: Ensemble Learning in Predicting Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete

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BUILDINGS 2024年第2期14卷 396页

作者： Wang, Ranran Zhang, Jun Lu, Yijun Huang, Jiandong Guangzhou Univ Sch Fine Arts & Design Guangzhou 510006 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China

Fiber-reinforced nano-silica concrete (FrRNSC) was applied to a concrete sculpture to address the issue of brittle fracture, and the primary objective of this study was to explore the potential of hybridizing the grey wolf Optimizer (GWO) with four robust and intelligent ensemble learning techniques, namely XGBoost, LightGBM, AdaBoost, and CatBoost, to anticipate the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) for sculptural elements. The optimization of hyperparameters for these techniques was performed using the GWO metaheuristic algorithm, enhancing accuracy through the creation of four hybrid ensemble learning models: GWO-XGBoost, GWO-LightGBM, GWO-AdaBoost, and GWO-CatBoost. A comparative analysis was conducted between the results obtained from these hybrid models and their conventional counterparts. The evaluation of these models is based on five key indices: R2, RMSE, VAF, MAE, and bias, addressing an objective assessment of the predictive models' performance and capabilities. The outcomes reveal that GWO-XGBoost, exhibiting R2 values of (0.971 and 0.978) for the train and test stages, respectively, emerges as the best predictive model for estimating the compressive strength of fiber-reinforced nano-silica concrete (FrRNSC) compared to other models. Consequently, the proposed GWO-XGBoost algorithm proves to be an efficient tool for anticipating CSFrRNSC.

关键词： fiber-reinforced concrete Durable Sculptural Elements AdaBoost CatBoost LightGBM XGBoost grey wolf optimization algorithm machine learning

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An adaptive deep transfer learning method for bearing fault diagnosis

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MEASUREMENT 2020年 151卷 107227-000页

作者： Wu, Zhenghong Jiang, Hongkai Zhao, Ke Li, Xingqiu Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Bearing fault diagnosis has made some achievements based on massive labeled fault data. In practical engineering, machines are mostly in healthy and faults seldom happen, it's difficult or expensive to collect massive labeled fault data. To solve the problem, an adaptive deep transfer learning method for bearing fault diagnosis is proposed in this paper. Firstly, a long-short term memory recurrent neural network model based on instance-transfer learning is constructed to generate some auxiliary datasets. Secondly, joint distribution adaptation, a feature-transfer learning method, which is used to reduce the differences in probability distributions between an auxiliary dataset and target domain dataset. Finally, grey wolf optimization algorithm is introduced to adaptively learn key parameters of joint distribution adaptation. The proposed method is verified with two kinds of datasets, and the results demonstrate the effectiveness and robustness of the proposed method when the labeled fault data are scarce. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Feature-transfer learning Instance-transfer learning Long-short term memory recurrent neural network Joint distribution adaptation grey wolf optimization algorithm

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Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 186卷

作者： Dong, Jianhua Liu, Xiaogang Huang, Guomin Fan, Junliang Wu, Lifeng Wu, Jie Nanchang Inst Technol Sch Hydraul & Ecol Engn Nanchang 330099 Jiangxi Peoples R China Kunming Univ Sci & Technol Fac Agr & Food Kunming 650500 Yunnan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Wuhan Polytech Univ Sch Civil Engn & Architecture Wuhan 430023 Peoples R China

Accurate estimation of reference crop evapotranspiration (ET0) is of great significance to crop water use and agricultural water resources management. This study evaluated the performance of four bio-inspired algorithm optimized kernel-based nonlinear extension of Arps decline (KNEA) models, namely KNEA with grasshopper optimization algorithm (GOA-KNEA), KNEA with grey wolf optimizer algorithm (GWO-KNEA), KNEA with particle swarm optimization algorithm (PSO-KNEA), and KNEA with salp swarm algorithm (SSA-KNEA), on estimating monthly ET0 across China. Monthly meteorological data [including maximum air temperature (Tmax), minimum air temperature (Tmin), extra-terrestrial solar radiation (Ra), relative humidity (RH), global solar radiation (Rs), wind speed (U)] during 1966-2015 from 51 weather stations across the seven different climate zones of China were used for model training and testing. Four different combinations of meteorological data were applied as model input, and results from the FAO-56 Penman-Monteith formula were used as a control. Results showed that the GWO-KNEA model overall performed better than the other three coupling models, of which the GWO-KNEA2 model (i.e., the model with input combination 2) was the best (on average R2 = 0.9814, RMSE = 0.2143 mm d-1). The convergence rate and the population size of the GWO-KNEA model were also superior to the other three models. Among input combinations, models with combination 2 had the best overall performance, while models with combination 3 were the worst on average. In terms of the importance of each meteorological parameter contributing to model accuracy, Rs was greater than Ra, RH, or U. Among different climate zones, station specific models in the semi-arid steppe of Inner Mongolia showed the best estimating performance in general, while models in the Qinghai-Tibetan Plateau overall performed relatively poorly. The GOA-KNEA and SSA-KNEA models with the combination 4 showed large increases (29.7% and 28.7

关键词： Reference evapotranspiration Climatic zones Grasshopper optimization algorithm grey wolf optimization algorithm Machine learning

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