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grey wolf optimisation algorithm for solving distribution network reconfiguration considering distributed generators simultaneously

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENERGY 2022年第11期41卷 2121-2149页

作者： Pujari, Harish Kumar Rudramoorthy, Mageshvaran Vellore Inst Technol Sch Elect Engn Vellore Tamil Nadu India

This article represents an application of the grey wolf optimisation (GWO) algorithm to solve the most optimistic combinatorial problems for optimal distribution network reconfiguration (DNR) and allocation of distributed generators (DGs) in a system. In this work, a metaheuristics algorithm is utilised to minimise the active power losses (APL) and enhance the voltage profile. Various scenarios were considered in this context to compare the performance of the proposed algorithm under voltage and current capacity constraints. Furthermore, a detailed validation via comparison of the results is being carried out with other methods from the exhaustive literature. The proposed algorithm reduces the APL by 63.13%, 56.19%, and 34.27% with DNR in IEEE 33, 69 and 118-bus systems. Similarly, APL reduction by 69.61%, 82.09%, and 36.08% with DNR considering DGs simultaneously. The results show that the proposed algorithm is an effective and promising method to solve problems similar to this work.

关键词： Distributed generations grey wolf optimisation algorithm loss reduction voltage profile network reconfiguration radial distribution systems

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Multi-Objective Planting Structure optimisation in an Irrigation Area Using a grey wolf optimisation algorithm

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WATER 2024年第16期16卷 2297页

作者： Wu, Li Tian, Junfeng Liu, Yanli Wang, Yong Zhang, Peixin Henan Univ Urban Construct Sch Municipal & Environm Engn Pingdingshan 467000 Peoples R China Nanjing Hydraul Res Inst State Key Lab Hydrol Water Resources & Hydraul Eng Nanjing 210029 Peoples R China Yangtze Inst Conservat & Dev Nanjing 210098 Peoples R China Minist Water Resources Res Ctr Climate Change Nanjing 210029 Peoples R China

To improve agricultural production efficiency, increase farmers' income, and promote sustainable development, we established a multi-objective optimisation model for crop planting structure in an irrigation area using the grey wolf optimisation (GWO) algorithm to comprehensively consider the resource, economic, and social objectives associated with agriculture. This model was subsequently applied to obtain the optimal planting structure in the southern bank of the Xiaolangdi Reservoir irrigation area in Henan Province, China. The planting areas of wheat, corn, autumn miscellaneous, and economic crops are 30,417;25,050;7157;and 1789 hm2, respectively. The irrigation water is 8292.66 x 104 m3, output value of crops is 105,721.37 x 104 CNY, and crop yield is 34,280.31 x 104 kg. Different solutions are used to solve the model to evaluate the results, and the order degree entropy method is used to evaluate and compare the results of multiple solutions. The optimisation scheme obtained with this model is consistent with the evaluation results of the cooperative game optimisation scheme, and the relative order degree entropy is 0.136, which is better than that in other schemes. Thus, the optimisation scheme of crop planting structure obtained via GWO comprehensively considers irrigation water consumption, economic benefits, and crop yield, which ensures coordinated development of resource, economic, and social systems and is conducive to promoting the benign development of the whole irrigation area system.

关键词： grey wolf optimisation algorithm planting structure order degree entropy sustainable development

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Operational-based techno-economic PMU installation approach using grey wolf optimisation algorithm (GWOA)

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第1期14卷 70-78页

作者： Maji, Tapas Kumar Acharjee, Parimal Natl Inst Technol Durgapur Dept Elect Engn Durgapur India

Phasor measurement units (PMUs) are currently the most useful devices to obtain fast, precise, and synchronised measurement data. Owing to the high cost of installation, the minimum number of PMUs required for the full observability of a network must be determined. However, power companies do not consider such optimal PMU installation (OPI), because their main concerns are preventing cascaded tripping and secure grid separation, for which critical buses (CBs) should be prioritised for direct observability. In practice, utility grids (UGs) may lose their full observability status during islanding, which the OPI problem should account for. On the other hand, actual bus-to-bus variations in the PMU installation cost are not properly addressed in the PMU installation problem. Considering these practical aspects, an operational-based multiphase PMU installation approach is proposed here using the grey wolf optimisation algorithm for CB prioritisation. A PMU cost minimisation-based OPI problem that considers the full observability of each UG during islanding is formulated. To determine quality outputs, logical and unique mathematical equations are developed. A novel multiple bus selection approach is proposed for a phase-wise bus assortment. The results of the proposed algorithm are compared with those of other existing methods.

关键词： phasor measurement synchronisation power system security power grids distributed power generation power distribution faults power generation economics power distribution economics power generation faults minimisation mathematical analysis synchronised measurement data optimal PMU installation cascaded tripping secure grid separation utility grids islanding bus-to-bus variations multiphase PMU installation approach grey wolf optimisation algorithm PMU cost minimisation-based OPI problem phase-wise bus assortment phasor measurement units operational-based technoeconomic PMU installation approach multiple bus selection approach GWOA grid separation security critical buses CB UG mathematical equations

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Equal-weight and rank-sum-weight-based systematic diminution of higher-order continuous systems using grey wolf optimisation

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2023年第1期43卷 64-79页

作者： Yadav, Umesh Kumar Patnana, Naresh Meena, V. P. Singh, V. P. Malaviya Natl Inst Technol Elect Engn Dept Jaipur Rajasthan India GITAM Univ Elect & Elect Engn Dept Visakhapatnam Andhra Pradesh India

The order-diminution techniques are adopted in various fields of engineering and applications to reduce the order of systems from higher-order to desired lower-order. In this research proposal, diminution of higher-order-continuous-systems (HOCSs) is done by incorporating systematic procedures for determination of weights. The errors between time-moments and Markov-parameters of HOCS and desired reduced-order-model (ROM) are utilised to frame the objective function. In the objective function, associated weights are determined using systematic procedures. The systematic procedures exploited in this work are equal-weight method and rank-sum-weight method. The minimisation of framed objective function is done by grey-wolf-optimisation algorithm. The effectiveness and superiority of proposed method is claimed with the help of tenth-order and seventh-order system by considering as test cases. The comparative analysis is done by tabulating time-domain specifications and error-indices. The responses of the HOCS and ROMs are also presented, to prove the efficacy and effectiveness of the proposed method.

关键词： higher-order continuous systems HOCSs grey wolf optimisation algorithm order-diminution reduced-order model ROM systematic procedures

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Research on Employee Performance Management Method Based on Big Data Improvement GWO-DELM algorithms

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第3期11卷 1-11页

作者： Wang, Zhuyu Liu, Yue Hainan Vocat Univ Sci & Technol Haikou 571127 Hainan Peoples R China Wenzhou Polytech Wenzhou 32500 Zhejiang Peoples R China

INTRODUCTION: Accurate and objective human resources performance management evaluation methods are conducive to a comprehensive understanding of the real and objective situation of teachers, and are conducive to identifying the management, teaching and academic level of teachers, which enables teacher managers to have a clear understanding of the gaps and problems among teachers. OBJECTIVES: Aiming at the current human resources performance management evaluation method, there are evaluation indexes exist objectivity is not strong, poor precision, single method and other problems. METHODS: This research puts forward an intelligent optimisation algorithm based on the improvement of the depth of the limit of the learning machine network of human resources performance management evaluation method. (1) Through the analysis of the problems existing in the current human resources performance management, select the human resources performance management evaluation indexes, and construct the human resources performance management evaluation system;(2) Through the multi -strategy grey wolf optimization algorithm method to improve the deep learning network, and construct the evaluation model of the human resources performance management in colleges;(3) The analysis of simulation experiments verifies the high precision and real-time nature of the proposed method. RESULTS: The results show that the proposed method improves the precision of the evaluation model, improves the prediction time. CONCLUSION: This research solves the problems of low precision and non-objective system indicators of human resource performance management evaluation.

关键词： human resource performance management evaluation deep extreme learning machine grey wolf optimisation algorithm Levy flight strategy

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High-performance control for a permanent-magnet linear synchronous generator using state feedback control scheme plus grey wolf optimisation

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IET ELECTRIC POWER APPLICATIONS 2020年第5期14卷 771-780页

作者： Sun, Xiaodong Wu, Minkai Yang, Zebin Lei, Gang Guo, Youguang Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang Jiangsu Peoples R China Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia

This study proposes an optimal control scheme for a permanent-magnet linear synchronous generator (PMLSG) using the state feedback control (SFC) method plus the grey wolf optimisation (GWO) algorithm. First, A novel state-space model of linear PMLSG is established in order to obtain desired dynamics and enough power when used for the smooth wave energy. Second, the GWO algorithm is adopted to acquire weighting matrices Q and R in the process of optimising linear quadratic regulator (LQR). What is more, a penalty term is brought into the fitness index to reduce the overstrike of output voltage and keep the rate of work more stable. Finally, optimal LQR-based SFC with and without penalty term and proportional-integral (PI) controllers are compared both in simulations and in experiments. Results clearly prove that the proposed optimal control strategy performs a better response when compared to other strategies.

关键词： synchronous generators permanent magnet generators state-space methods control system synthesis nonlinear control systems PI control optimisation machine control feedback linear quadratic control state feedback permanent-magnet linear synchronous generator state feedback control scheme optimal control scheme state feedback control method grey wolf optimisation algorithm state-space model linear PMLSG GWO algorithm linear quadratic regulator penalty term optimal LQR-based SFC PI controllers optimal control strategy

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Medical fusion framework using discrete fractional wavelets and non-subsampled directional filter banks

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IET IMAGE PROCESSING 2020年第4期14卷 658-667页

作者： Kaur, Gurpreet Singh, Sukhwinder Vig, Renu Punjab Univ UIET Dept Comp Sci & Engn Chandigarh India Punjab Univ Dept Elect Engn & Commun UIET Chandigarh India

Image fusion in neuro diagnosis is intimidating due to its complexity. The heterogeneous natures of the original brain images make intermodal transmission difficult during fusion. Medical image fusion using complementary modalities results in loss of vital salient information. Poor fusion, colour deficiencies result due to similar processing for both the modalities. A dual technique is proposed using discrete fractional wavelet transform (FRWT) and non-subsampled directional filter banks for better extraction of salient image elements for improved diagnosis. The sparsity character of the coefficients FRWT is controlled by optimising the parity operator using grey wolf optimisation algorithm. Four sets of neurological multimodal magnetic resonance imaging and single photon emission computed tomography (CT) brain images are used from benchmark database for validation. The objective evaluation has been conducted using five metrics. The main values obtained from objective metrics based on the proposed technique are 6.3213 for Shannon entropy, mutual information is computed to be 2.7582, fusion factor is 1.9095, standard deviation is 0.1310, and edge strength is 0.76122 indicating improved diagnostic information and superior image quality. Subjective evaluation by a medico validates the findings with finer visual output and enhanced contrast in comparison with recent and state-of-the-art methods.

关键词： neurophysiology single photon emission computed tomography medical image processing entropy image fusion channel bank filters brain biomedical MRI feature extraction wavelet transforms neuro diagnosis brain image intermodal transmission medical image fusion vital salient information colour deficiencies salient image elements grey wolf optimisation algorithm neurological multimodal magnetic resonance imaging diagnostic information superior image quality medical fusion framework nonsubsampled directional filter banks single photon emission computed tomography brain image discrete fractional wavelet transform Shannon entropy

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