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Ladder network parameters identification of an isolated winding by combining the intelligent optimisation algorithm and GNIA

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第2期13卷 296-304页

作者： Ren, Fuqiang Xiao, Yao Zhan, Cao Liu, Yong Yang, Fan Ji, Shengchang Zhu, Lingyu Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian 710049 Shaanxi Peoples R China Zhongyuan Univ Technol Sch Elect Informat Zhengzhou 450007 Henan Peoples R China China Elect Power Res Inst High Voltage Res Inst Beijing 100192 Peoples R China

The ladder network parameter identification for transformer winding is crucial for the interpretation of the frequency response function data. The traditional identification method, mainly based on intelligent optimisation algorithm, is generally very time-consuming due to a large amount of computation. This study proposes to combine the intelligent algorithm and Gauss-Newton iteration algorithm (GNIA) to improve the optimisation efficiency notably with a sharply dropped calculation workload. These two methods are well-complementary since the intelligent algorithm holds excellent global search ability while the search of the GNIA is directional and quantitative. This study solves three key problems for the combined algorithms. The first problem is the calculation of the least-square correction solution to the network parameters in the iteration algorithm. The treatment of the ill-conditioned Jacobian matrix in the iteration algorithm is the second challenge. Another issue is the determination of the network parameter with zero sensitivity. The identification results on an isolated winding show that the combined algorithms can obtain a more precise solution with far less amount of computation.

关键词： least squares approximations Jacobian matrices Newton method transformer windings optimisation genetic algorithms frequency response search problems parameter estimation iterative methods combined algorithms ladder network parameters identification intelligent optimisation algorithm GNIA ladder network parameter identification transformer winding frequency response function data traditional identification method intelligent algorithm Gauss-Newton iteration algorithm optimisation efficiency sharply dropped calculation workload excellent global search ability identification results isolated winding show

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Development and application of a low-cost and portable multi-channel spectral detection system for mutton adulteration

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BIOSYSTEMS ENGINEERING 2024年 247卷 13-25页

作者： Wang, Shichang Fan, Binbin Huang, Zhongtao Bai, Zongxiu Zhu, Rongguang Meng, Lingfeng Shihezi Univ Coll Mech & Elect Engn Shihezi 832003 Xinjiang Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Agr Equipment Shihezi 832003 Peoples R China Chinese Acad Agr Sci Tobacco Res Inst Qingdao 266000 Peoples R China Anhui Acad Agr Sci Agr Engn Res Inst Hefei 230001 Peoples R China

It is important to develop low-cost, fast and portable meat adulteration detection systems to ensure the meat authenticity and safety in complex market environments. A multi-channel spectral detection system for meat adulteration was developed in this study. The core hardware of the system mainly includes a designed spectral module and a Raspberry pi controller. The spectral module consists of three multi-channel spectral sensors and LED lamps with specific wavelengths, containing 18 channels covering a range of 410-940 nm. The software was developed based on PyQt5. After completing the construction of the system, the detection distance was discussed and determined to be 4 mm. Based on the spectral data collected by the developed system, the models for classifying pure mutton, pure pork, mutton flavour essence adulteration, colourant adulteration and adulterated mutton with pork were established and compared. Four intelligent optimisation algorithms were further used to improve the model performance. The results of the test set showed that the support vector classification (SVC) model optimised by a sparrow search algorithm (SSA) obtained the best classification performance, with an accuracy of 97.59% and a Kappa coefficient of 0.9696. After the SSA-SVC was incorporated into the sensor software, the system performance was evaluated using external validation samples. The overall accuracy of the system was 94.29%. The system took about 5.31 s to detect a sample, and the total weight of the system was 1.55 kg. Overall, the developed portable spectral system has considerable potential to rapidly and accurately discriminate adulterated mutton in the field.

关键词： Mobile spectroscopy Meat sensing System development intelligent optimisation algorithm

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Systematic learning-based system approximation using rank-exponent method exploiting GWO algorithm

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JOURNAL OF CONTROL AND DECISION 2024年第3期11卷 450-459页

作者： Yadav, Umesh Kumar Mande, Praveen Singh, V. P. MNIT Elect Engn Dept Jaipur India GITAM EED Visakhapatnam India MNIT Elect Engn Dept Jaipur 302017 India

In this proposal, systematic learning-based approximation of higher-order (HO) system is performed utilising rank-exponent (RE) assisted weight determination method incorporating grey-wolf-optimisation (GWO) algorithm. The time-moments (TMs) along with Markov-parameters (MPs) of HO system and desired approximant are ascertained in order to utilise in the approximation process. The errors between TMs as well as MPs of HO system and desired approximant are used to frame weighted objective-function. The weights associated with objective-function are derived systematically using RE method. Once, weights are derived using RE method, GWO algorithm is used for minimising the resultant objective-function. The sixth-order system of hydro-power-station is utilised as real-test scenario in this study to demonstrate efficacy and effectiveness of proposed RE-based methodology. The considered sixth-order fixed-coefficient HO system is approximated to reduced-order-model of order three. For better examination and analysis of proposed methodology, responses along with quantitative analysis in tabular form for time-domain-specifications and performance-error-indices are presented.

关键词： Approximation decision-making integration of decision-making and control intelligent optimisation algorithm rank-exponent method systematic process

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Simulation of Giant-magnetostrictive actuator control system optimized by sparrow algorithm with positive cosine-Cauchy variation 6

Simulation of Giant-magnetostrictive actuator control system...

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2023 IEEE 6th International Conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Chu, Changbao Zeng, Huangjun Jia, Xingjian Xi, Junmei Nanchang Institute of Technology Department of Mechanical Engineering Nanchang China Nanchang Institute of Technology Jiangxi Key Laboratory of Precision Drive and Control Nanchang China Nanchang Institute of Technology Department of Electrical Engineering Nanchang China

ISBN: (纸本)9798350313444

Aiming at the deficiency of output displacement hysteresis in the precision control of the Giant-magnetostrictive actuator, the sparrow search algorithm of the positive cosine-Cauchy variation is combined with the PID control. Based on the piezomagnetic equation and the kinetic model of the actuator, the dynamic linear model of the input current and output displacement of the Giant-magnetostrictive actuator was established. The PID optimisation model of the Giant-magnetostrictive actuator is built by combining Matlab and Simulink, and the automatic adjustment of the PID parameters is achieved. The simulation results show that the sparrow search algorithm with positive cosine-Cauchy variation can improve the hysteresis of the output displacement to a larger extent, and improve the control accuracy and robustness of the controller. © 2023 IEEE.

关键词： Actuator giant magnetostrictive material intelligent optimisation algorithm PID control

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Big-data-driven pre-stack seismic intelligent inversion

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INFORMATION SCIENCES 2021年 549卷 34-52页

作者： Yan, Xuesong Zhang, Mingzhao Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Peoples R China

Seismic inversion is an image formation process for the spatial structures and physical properties of underground rock strata according to the seismic data observed on the surface or in wells under the constraints of known geological laws and drilling and logging data. It is an important component of geophysical inversion. The inversion of reservoir parameters using seismic data is a non-linear problem. Therefore, the use of linear or quasi-linear methods to solve this problem makes it easy to fall into local optimal solutions. In contrast, intelligent optimisation algorithms based on the global optimum have strong local and global optimisation abilities and good convergence. Thus, they can improve the calculation efficiency and are suitable for geophysical inversion problems with multiple parameters and multiple extremums. In this study, the pre-stack seismic inversion problem is considered as the research object. According to the non-linear characteristics of the problem, the pre-stack seismic intelligent inversion method with a hybrid genetic algorithm is proposed, which solves the problems for which the standard genetic algorithm may easily fall into local optimums, resulting in an indistinct inversion effect, especially an unsatisfactory optimisation effect for density parameters. The experimental results show that the inversion parameters fit well with the theoretical model logging curve. In view of the high data volume in inversion, a new parallel strategy is proposed and combined with the MapReduce framework. This strategy can reduce the operating time while maintaining the diversity of the population. The strategy is implemented on the Hadoop platform and its effectiveness is verified. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Seismic inversion Pre-Stack seismic data Elastic parameters intelligent optimisation algorithm MapReduce

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WOLF COLONY IMPROVEMENT algorithm IN intelligent LOGISTICS SUPERVISION SYSTEM

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MECHATRONIC SYSTEMS AND CONTROL 2024年第2期52卷 101-109页

作者： Zhang, Taizhen Xu, Jianhui Sichuan Engn Tech Coll Dept Econ & Management Deyang 618000 Peoples R China

With the rapid advancement of the logistics industry, intelligent logistics (IL) technology has become a research hotspot for scholars. Among them, the wolf colony algorithm (WCA), as a new intelligent optimisation algorithm, has received widespread attention, but because of its short time, it still has the problem of low computational efficiency. Therefore, the research first carried out the software and hardware design of the IL supervision system, and then improved the calculation methods of walking, summoning and besieging behaviours in the WCA to improve the global convergence and computing efficiency of the algorithm. Finally, during the attacking process, adaptive weights (AWs) are added to automatically adjust the step size, and a improved wolf swarm algorithm is designed and applied to the IL supervision system. The research outcomes demonstrate the variance of the improved algorithm is 7.7826 x 10 - 9 and 1.6144 x 10-14 , respectively, and its stability is better. In the iterative convergence curve, the improved algorithm has a faster convergence speed, with a shortest path of 15574.58 and higher accuracy. In the calculation of the optimal value of logistics path, the improved algorithm has a value of 5.31 x 10 5 , which is superior to other algorithms. This indicates that the algorithm has high stability, efficiency, and accuracy, providing better technical support for IL supervision systems.

关键词： intelligent optimisation algorithm wolf colony algorithm logistics supervision system adaptive weight

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intelligent inversion method for pre-stack seismic big data based on MapReduce

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COMPUTERS & GEOSCIENCES 2018年第Jan.期110卷 81-89页

作者： Yan, Xuesong Zhu, Zhixin Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China

Seismic exploration is a method of oil exploration that uses seismic information;that is, according to the inversion of seismic information, the useful information of the reservoir parameters can be obtained to carry out exploration effectively. Pre-stack data are characterised by a large amount of data, abundant information, and so on, and according to its inversion, the abundant information of the reservoir parameters can be obtained. Owing to the large amount of pre-stack seismic data, existing single-machine environments have not been able to meet the computational needs of the huge amount of data;thus, the development of a method with a high efficiency and the speed to solve the inversion problem of pre-stack seismic data is urgently needed. The optimisation of the elastic parameters by using a genetic algorithm easily falls into a local optimum, which results in a non-obvious inversion effect, especially for the optimisation effect of the density. Therefore, an intelligent optimisation algorithm is proposed in this paper and used for the elastic parameter inversion of pre-stack seismic data. This algorithm improves the population initialisation strategy by using the Gardner formula and the genetic operation of the algorithm, and the improved algorithm obtains better inversion results when carrying out a model test with logging data. All of the elastic parameters obtained by inversion and the logging curve of theoretical model are fitted well, which effectively improves the inversion precision of the density. This algorithm was implemented with a MapReduce model to solve the seismic big data inversion problem. The experimental results show that the parallel model can effectively reduce the running time of the algorithm.

关键词： intelligent optimisation algorithm Pre-stack seismic data Elastic parameter inversion MapReduce

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Novel methodology solving distribution network reconfiguration with DG placement

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 1668-1674页

作者： Gong, Zhiyun Chen, Qing Sun, Kongming Shandong Univ Sch Elect Engn Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan Shandong Peoples R China

This paper proposes an effective and accurate methodology to solve the smart distribution network reconfiguration problem considering distributed generation (DG) placement, whose objective is to minimise power loss and enhance voltage stability. An intelligent optimisation algorithm using the electromagnetism-like mechanism (ELM) is used to effectively find the best configuration of the network and placement of the DG units simultaneously. ELM is a novel global optimisation algorithm referring to the electromagnetic interaction among the electric charge to search a best location of an individual. To secure the radial nature of the network and avoid creating non-feasible solutions, a novel encoding and decoding method is designed based on an exchanging tie switch sequentially, thereby accelerating the calculation speed. The rationality of codification is theoretically certified thereafter. To assess the performance of the proposed method, simulations are carried out on the IEEE 33- and 69-bus distribution network. The simulation results demonstrate the validity and effectiveness of the proposed method.

关键词： power distribution planning optimisation distribution networks distributed power generation novel global optimisation algorithm electromagnetic interaction creating nonfeasible solutions 69-bus distribution network novel methodology solving distribution network reconfiguration DG placement effective methodology accurate methodology smart distribution network reconfiguration problem generation placement power loss voltage stability intelligent optimisation algorithm electromagnetism-like mechanism ELM DG units

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Data driven output joint probability density function control for multivariate non-linear non-Gaussian systems

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IET CONTROL THEORY AND APPLICATIONS 2015年第18期9卷 2697-2703页

作者： Yin, Liping Zhang, Hongyan Guo, Lei Nanjing Univ Informat Sci & Technol CICAEET Nanjing 210044 Jiangsu Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100083 Peoples R China

This study presents a novel data-based joint probability density function (JPDF) control strategy for multivariate non-linear non-Gaussian stochastic systems so that the output JPDF of the system can be made to follow a desired JPDF. The output JPDF, which is usually immeasurable, is estimated according to the output sequence of the system. The multi-step-ahead cumulative performance index is constructed with respect to the control objective and is minimised based on an intelligent optimisation algorithm. By minimising this performance function, a new predictive controller design algorithm is established with more simple formulation and less computation load than existed results. Furthermore, a new approach is developed to guarantee convergence in distribution'. Finally, simulations are given to demonstrate the effectiveness of the proposed control algorithm and some desired results have been obtained.

关键词： probability nonlinear control systems multivariable control systems predictive control control system synthesis optimisation stochastic systems data driven output joint probability density function control multivariate nonlinear nonGaussian systems data-based joint probability density function control strategy JPDF control strategy predictive controller design algorithm intelligent optimisation algorithm multistep-ahead cumulative performance index stochastic systems

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