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GWO-Potential Field Method for Mobile Robot Path Planning and Navigation Control

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第8期46卷 8087-8104页

作者： Zafar, Mohd Nayab Mohanta, J. C. Keshari, Anupam MNNIT Allahabad Dept Mech Engn Prayagraj India Natl Inst Ind Engn Mumbai Maharashtra India

A navigation control methodology is proposed in this paper, which utilizes a hybrid concept of grey wolves optimisation (GWO) algorithm and an artificial potential field (APF) method for on-time path planning of mobile robot. The proposed methodology runs in two folds. The first fold defines the focus region (FR), which shows the obstacle-free locations of the all possible robot movements. However, in the second fold step GWO algorithm searches the shortest path by minimizing the artificial potential field value of the location generated within FR. The presented methodology was simulated and verified in various real and simulation environments. The simulation and experimental results reveal that the presented methodology can not only be capable of finding an optimal or near-optimal robot-path in a complex obstacle-present environment but also provides an effective path planning strategy on-time basis. The results show that the proposed method has out-performed to shorten the path length as well as ensured collision-free navigation. At the same time, virtual intermediate targets (VITs) makes the navigation free from any dead-end situation, even in a cluttered environment.

关键词： Mobile robot navigation grey wolf optimization algorithm Artificial potential field Collision avoidance

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Adaptable algorithm for Tracking Global Maximum Power Point of Photovoltaic Module Arrays

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SENSORS AND MATERIALS 2023年第12期35卷 4359-4381页

作者： Chao, Kuei-Hsiang Kuo, Ying-Piao Chen, and Hong -Han Natl Chin Yi Univ Technol Dept Elect Engn 57Sect 2Zhongshan Rd Taichung 41170 Taiwan

The main objective of this study was to develop a method for the maximum power point tracking (MPPT) of photovoltaic module arrays (PVMAs) implemented using two proposed improved grey wolf optimization algorithms (GWOAs). A high-step-up soft-switching converter combined with lost-cost voltage and current sensors was adopted to realize MPPT. This reduced the converter switching losses and cost. Furthermore, in the improved GWOAs, iteration parameters were automatically adjusted online on the basis of the slope of the power-voltage (P-V) curve of the PVMA. In addition, 0.8 times the maximum power point (MPP) voltage of the PVMAs under standard test conditions was set as the starting voltage for conducting global MPPT. Lastly, by verifying the proposed improved GWOAs with actual test results, where multiple peak values were generated in the P-V characteristic curve of the PVMA by shading, we demonstrated that all MPPs could be tracked successfully, and the two improved GWOAs reduced the tracking time by at least 18.6 and 33.3% compared with that of the conventional GWOA. Therefore, the improved GWOAs exhibit superior tracking speed and stability.

关键词： grey wolf optimization algorithm photovoltaic module array global maximum power point tracking partial shading high step-up soft-switching converter multiple peak values of power-voltage characteristic curve

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Remote Sensing Inversion of Typical Offshore Water Quality Parameter Concentration Based on Improved SVR algorithm

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REMOTE SENSING 2023年第8期15卷 2104-2104页

作者： Ren, Jianghua Cui, Jianyong Dong, Wen Xiao, Yanfang Xu, Mingming Liu, Shanwei Wan, Jianhua Li, Zhongwei Zhang, Jie China Univ Petr Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Technol Innovat Ctr Maritime Silk Rd Marine Resour Qingdao 266580 Peoples R China Chinese Acad Sci Acad Aerosp Informat Innovat Beijing 100094 Peoples R China Minist Nat Resources Inst Oceanog 1 Remote Sensing Off Qingdao 266061 Peoples R China

Chlorophyll a concentration and suspended matter concentration, as typical water quality parameters related to spectral characteristics, are essential for characterizing the degree of eutrophication in water bodies. They have become crucial indicators for water quality assessment of inland water bodies. The support vector regression model (SVR) is suitable for small samples, has excellent generalization ability, and has high prediction accuracy. Still, it has the problem of difficult selection of model parameters and quickly falling into local extremes. To solve this problem, a hybrid Differential Evolution-grey wolf Optimizer (DE-GWO) algorithm is introduced into the parameter selection process of the support vector regression model, and an improved SVR algorithm (DE-GWO-SVR) is proposed for the remote sensing inversion of chlorophyll a concentration and suspended sediment concentration in water bodies. In this paper, the spectral reflectance of the water surface and the chlorophyll a and broken matter concentration values were obtained by field measurements in the Tangdao Bay waters of Qingdao, Shandong Province. The inverse model between the concentration values of the two water quality parameters and the corresponding sensitive factors was established by first determining the sensitive factors based on the response of the spectral reflectance to the two water quality parameters and introducing the DE-GWO optimization algorithm into the parameter selection process of the SVR model. Finally, the accuracy of the model was verified using Sentinel II satellite remote sensing spectral data, and then the inverse accuracy of the two water quality parameters was obtained. The mean relative error (MRE) of the chlorophyll a prediction model built by the DE-GWO algorithm optimizing the SVR is 25.1%, and the mean relative error (MRE) of the suspended matter prediction model is 32.5%. The inversion results were all better than the other models (linear regression, SVR, and GWO

关键词： chlorophyll a suspended matter SVR differential evolution algorithm grey wolf optimization algorithm

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Digital twin-based job shop anomaly detection and dynamic scheduling

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2023年 79卷

作者： Li, Yibing Tao, Zhiyu Wang, Lei Du, Baigang Guo, Jun Pang, Shibao Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China KTH Royal Inst Technol Dept Prod Engn Stockholm Sweden Wuhan Univ Technol Hubei Digital Mfg Key Lab Wuhan 430070 Peoples R China

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.

关键词： Digital twin (DT) Multi-level production process monitoring model Real-time scheduling optimization strategy Rolling window mechanism grey wolf optimization algorithm

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Data collection optimization of ocean observation network based on AUV path planning and communication

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OCEAN ENGINEERING 2023年第1期282卷

作者： Yan, Z. Li, Y. Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Natl Univ Singapore Coll Design & Engn Singapore 119077 Singapore Technol Ctr Offshore & Marine Singapore TCOMS Singapore 118411 Singapore

The Ocean Observation Network (OON) is an ocean science and engineering observation platform which integrates underwater wireless sensors, acoustic buoys, submersible buoys, and sonars. The complex underwater environment makes it challenging to place the observation nodes flexibly. In this research, firstly, a novel OON data collection optimization scheme is proposed, which is based on Autonomous Underwater Vehicle (AUV) path planning and communication. By combining the Voronoi diagram and K-means algorithm, an optimized clustering algorithm (VorK-means) is proposed to optimize the location of junction boxes and reduce the losses in the transmission energy of submarine cables in the OON simultaneously. Subsequently, a computational algorithm for AUV data collection position is proposed based on Voronoi vertices and communication circles. Moreover, an improved grey wolf optimization (GWO) algorithm, based on the Levy flight and dimension (LD) search strategy, is proposed and applied for the AUV path planning, which is named LD-GWO. The accuracy and efficiency are validated by applying the proposed algorithms to the Canadian North-East Pacific Time-series Underwater Networked Experiment (NEPTUNE). Compared with the K-means algorithm, the VorKmeans algorithm can reduce the total length of submarine cables by nearly 14%, and compared with the GWO algorithm, LD-GWO can obtain shorter data collection paths under identical parameter settings.

关键词： Ocean observatory networks grey wolf optimization algorithm Path planning and communication

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Improved GWO-MCSVM algorithm based on nonlinear convergence factor and tent chaotic mapping and its application in transformer condition assessment

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期224卷

作者： Zhang, Qizhao Liu, Hongshun Guo, Jian Wang, Yifan Liu, Luyao Liu, Hongzheng Cong, Haoxi Shandong Univ Sch Elect Engn Shandong Prov Key Lab UHV Transmiss Technol & Equi Jinan Peoples R China State Grid Qingdao Power Supply Co Qingdao Peoples R China North China Elect Power Univ Sch Elect Engn Beijing Peoples R China

The continuous and reliable operation of the transformer is the basis to ensure the normal operation of the power system. Relevant departments collect multi-dimensional and multi-source heterogeneous parameter data during the operation, maintenance and repair of transformers. The effective information contained in the parameter data can directly reflect the current operating status of the transformer. On the basis of support vector machine and grey wolf algorithm, an improved GWO-MCSVM algorithm based on nonlinear convergence factor and Tent chaotic mapping is proposed. The algorithm parameters are optimized through training samples, and the results are evaluated and verified in the algorithm itself, so as to improve the accuracy of the status assessment results. Finally, the accuracy of assessment results of the algorithm proposed in this paper, existing genetic algorithms and particle swarm optimization algorithms are compared by evaluating multiple sets of measured samples. By comparison, the effectiveness of the algorithm proposed in this paper for transformer condition assessment has been verified.

关键词： Transformer Condition assessment Support vector machine grey wolf optimization algorithm

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Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Guan, Shan Wu, Tong-yu Yang, Hai-qi Northeast Elect Power Univ Sch Mech Engn Jilin 132012 Peoples R China

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data;Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion;Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

关键词： Transformer fault diagnosis Unbalanced small samples ACGAN grey wolf optimization algorithm Least squares support vector machine

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Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control

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SENSORS 2023年第20期23卷 8388页

作者： Jin, Linhao Fan, Jingjing Du, Fu Zhan, Ming North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Beijing Inst Technol Sch Mech Vehicular Engn Beijing 100081 Peoples R China

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

关键词： two-stage ISD semi-active suspension grey wolf optimization algorithm fuzzy neural network Matlab/Simulink

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Type recognition of partial discharge source based on PCA and GWO-SVM 33

Type recognition of partial discharge source based on PCA an...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Li Li Chen Yuepeng Yang Guang Mao Cuimin Zhang Huajun Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China Hubei Elect Power Survey & Design Inst Co LTD Wuhan 430040 Hubei Peoples R China

ISBN: (纸本)9781665440899

As an important indication and manifestation of insulation failures in high-voltage equipment, the type identification of partial discharge (PD) is important for the assessment of the insulation state of electrical equipment. In order to identify the type of partial discharge source accurately, this paper presents a method for partial discharge source type identification based on principal component analysis (PCA) and support vector machine (SVM) with grey wolf optimization algorithm (GWO). The PCA method is used to select the three features out of 10 that best represent the original data, namely the phase, E/I-peak and I-peak/Q. On this basis, the SVM kernel function g and the non-negative penalty factor c are optimized by GWO to establish a support vector machine classification model based on the grey wolf optimization algorithm (GWO-SVM). The results show that PCA is able to extract the main features well and that GWO-SVM can identify partial discharge source more accurately than the genetic algorithm-based support vector machine (GA-SVM), multilayer perceptron (MLP) and k-nearest neighbor (kNN) classifier.

关键词： Principal component analysis support vector machines identification of partial discharge types grey wolf optimization algorithm k-Nearest neighbors multilayer perceptron

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Prediction of high-temperature flow stress of HMn64-8-5-1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

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MATERIALS RESEARCH EXPRESS 2022年第9期9卷 096506页

作者： Liang, Qiang Zhang, Xianming Liu, Xin Li, Yongliang Minist Educ Engn Res Ctr Waste Oil Recovery Technol & Equipme Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Coll Mech Engn Chongqing 400067 Peoples R China Chongqing Changjiang Electrician Ind Grp Co Ltd Chongqing 401336 Peoples R China

An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64-8-5-15 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 001-10 s(-1), the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3139 %, 2448 % and 1265 %, and the values of RMSE were calculated to be 1804, 1482 and 0467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64-8-5-15 alloy.

关键词： HMn64-8-5-1 5 brass alloy flow stress constitutive model BP neural network grey wolf optimization algorithm

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