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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Wu, Huaning Wang, Hengfeng Qin, Hua Xie, Xu Naval Univ Engn Coll Elect Engn Wuhan Peoples R China PLA Force 92145 Shanghai Peoples R China

ISBN: (纸本)9781728161365

In this paper, a new type of 10-meters single-loaded broadband twin-whip antenna is designed. The influence of antenna spacing on the performance of twin-whip antenna is analyzed by moment method. The loading position, the parameters of loading and broadband matching network of antenna is optimized and calculated by using algorithm. A 10:1 scaled model antenna has been fabricated, whose VSWR, gain, pattern and non-circularity are simulated and calculated. The measured VSWR is in agreement with the simulation results, which of all frequency points is less than 3 with an average value of 2.15. The maximum gain is greater than -3dB, the maximum is 5.29dB with an average of 3.39dB. The minimum non-circularity of gain is 0.03dB (at 30MHz), and the maximum is 2.74dB (at 230MHz). The efficiency is higher than 16.5%, the maximum was 68.7% with an average of 57.75%.

关键词： twin-whip antenna lumped loading broadband matching short wave grasshopper optimization algorithm

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Performance Improvement of Photovoltaic Emulator Using Lambert W Model and Fractional Order PI Controller 12

Performance Improvement of Photovoltaic Emulator Using Lambe...

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12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)

作者： Esfandiari, Sadegh Khaniki, Mohammad Ali Labbaf Montazeri, Seyed Hamid Milimonfared, Jafar Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran KN Toosi Univ Technol Fac Elect Engn Tehran Iran

ISBN: (纸本)9781665403665

A photovoltaic (PV) emulator replicates the exact I-V and P-V output characteristics of a PV module or array. This replication should have occurred in such a way the same output characteristics in different ambient conditions, including different temperature and irradiation levels, mismatching, or partial shading, have been obtained. In this paper, a PV emulator based on a DC-DC converter with a novel control method has been presented. The proposed controller is a fractional PI one optimized with grasshopper optimization algorithm (GOA). It has one degree of freedom more than the conventional PI controller, which is the main advantage of this control approach. Also, a fractional-order lag compensator is used to decrease the output ripple of the converter. On the other hand, the Lambert W function models the PV characteristics which this modeling approach reduces computational burden and time. The simulation results in MATLAB/Simulink validate the performance of the proposed PV emulator.

关键词： DC-DC converter Fractional-order PI controller grasshopper optimization algorithm Lambert W PV emulator

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Intelligent optimization of process parameters to corn straw pretreatment 34

Intelligent optimization of process parameters to corn straw...

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34th Chinese Control and Decision Conference (CCDC)

作者： Guan, Yalin Chen, Juan Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China

ISBN: (纸本)9781665478960

For the optimization of pretreatment process parameters of com straw, an intelligent optimization method is used, which combines the prediction model and the optimization algorithm. The support vector machine (SVM) prediction model for biogas production is constructed. And the optimal process parameters are obtained by grasshopper optimization algorithm (GOA) on the prediction model. Due to the difficulty of obtaining experimental data, the problem of small samples during the model training exists. So, the virtual samples are generated by uniform interpolation combined with GOA to improve the model prediction accuracy. Through the experiment of dual-frequency ultrasound combined with alkali pretreatment of corn stover fermentation, the effectiveness of virtual sample generation and the feasibility of intelligent process parameter search are verified.

关键词： Corn Straw optimization of Process Parameters Virtual Sample grasshopper optimization algorithm

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Hybrid GOA-SVR technique for short term load forecasting during periods with substantial weather changes in North-East India 8

Hybrid GOA-SVR technique for short term load forecasting dur...

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8th International Conference on Advances in Computing & Communications (ICACC)

作者： Barman, Mayur Choudhury, Nalin Behari Dev Natl Inst Technol Dept Elect Engn Silchar 788010 Assam India

The power system load demand is extremely prejudiced by thermal inertia caused by weather parameters. This influence even became stronger when there are vast and rapid changes in the weather parameters. Therefore it is necessary to consider the weather changes for a precise short term load forecasting (STLF). This work proposes a hybrid technique for conducting STLF during periods with substantial weather changes. The proposed technique is based on support vector regressing (SVR), grasshopper optimization algorithm (GOA) and a novel Euclidean norm (EN) based data processing unit. Here GOA is utilized for accessing the appropriate SVR parameters. The EN based data processing unit is applied for fixing the training data by assessing some alike days in the recent historicized days. The proposed GOA-SVR technique identifies the substantial changes in weather (temperature) and regulates the training data accordingly. This work is done in North-East India and the proposed technique is tested on the data of load dispatch center of Assam state. Three case studies are conducted and forecasting performances of the proposed technique are compared with the conventional technique of training the system with just recent historicized day's data. The proposed technique found outperformed the conventional technique in all cases. (C) 2018 The Authors. Published by Elsevier B. V.

关键词： Short term load forecasting Substantial weather changes Euclidean norm based data processing unit Support vector regression grasshopper optimization algorithm

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Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

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WORLD JOURNAL OF ENGINEERING 2022年第2期19卷 147-156页

作者： Kaur, Jasleen Rani, Punam Dahiya, Brahm Prakash CT Univ Dept Comp Applicat Sidhwan Khurd India BLM Girls Coll Nawanshahr India

Purpose This paper aim to find optimal cluster head and minimize energy wastage in WSNs. Wireless sensor networks (WSNs) have low power sensor nodes that quickly lose energy. Energy efficiency is most important factor in WSNs, as they incorporate limited sized batteries that would not be recharged or replaced. The energy possessed by the sensor nodes must be optimally used so as to increase the lifespan. The research is proposing hybrid artificial bee colony and glowworm swarm optimization [Hybrid artificial bee colony and glowworm swarm optimization (HABC-GSO)] algorithm to select the cluster heads. Previous research has considered fitness-based glowworm swarm with Fruitfly (FGF) algorithm, but existing research was limited to maximizing network lifetime and energy efficiency. Design/methodology/approach The proposed HABC-GSO algorithm selects global optima and improves convergence ratio. It also performs optimal cluster head selection by balancing between exploitation and exploration phases. The simulation is performed in MATLAB. Findings The HABC-GSO performance is evaluated with existing algorithms such as particle swarm optimization, GSO, Cuckoo Search, Group Search Ant Lion with Levy Flight, Fruitfly optimization algorithm and grasshopper optimization algorithm, a new FGF in the terms of alive nodes, normalized energy, cluster head distance and delay. Originality/value This research work is original.

关键词： Particle swarm optimization Glowworm swarm optimization Cuckoo search grasshopper optimization algorithm

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Harmonic Minimization using grasshopper Optimizer in Five-phase Multilevel Inverter

Harmonic Minimization using Grasshopper Optimizer in Five-ph...

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IEEE International Future Energy Electronics Conference (IFEEC)

作者： Chew, W. T. Yong, W., V Ong, S. L. Sea, Y. W. Ahmad, N. S. Leong, J. H. Univ Malaysia Perlis Fac Elect Engn Technol Perlis Malaysia

ISBN: (纸本)9781665434485

This paper presents the use of grasshopper optimization algorithm based selective harmonic minimization pulse-width modulation technique for optimizing the switching angles of five-phase nine-level multilevel inverter. The proposed algorithm is able to provide optimum switching angles in a wide range of modulation index. Besides, the switching angles obtained by the proposed algorithm can be used to synthesize an output voltage waveform with desired fundamental component and minimized undesired low order harmonics. MATLAB simulation results show the proposed algorithm can provide a wide modulation index range of optimum solutions. Furthermore, both MATLAB simulation and PSIM experimental results obtained from five-phase nine-level cascaded H-bridge multilevel inverter show good agreement, confirming the validity of switching angles calculated using the proposed algorithm.

关键词： multilevel inverter grasshopper optimization algorithm harmonic minimization

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Short-Term EVs Charging Load Prediction Under Low-quality Data Environment Based on GRU-GAN-GOA-MKELM Framework

Short-Term EVs Charging Load Prediction Under Low-quality Da...

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2024 International Seminar on Artificial Intelligence, Computer Technology and Control Engineering, ACTCE 2024

作者： Yang, Ye Wang, Wen Zhong, Yulu Jin, Chunhua Zhao, Chenfang Qiu, Baomin Jia, Wenjie State Grid Smart Internet of Vehicles Co. Ltd Beijing China Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798331521714

The precise forecasting of electric vehicles (EVs) load can provide reference for assessing the influence of EVs on the electricity network and optimizing the operation and allocation of the electricity network. Nevertheless, as the recorded dataset of the EV battery charging stations are difficult to collect, obtaining accurate prediction results with scarce EVs load data is challenging. On the one hand, the missing data exert great influence on forecasting values. On the other hand, a forecasting framework with superior precision should be trained with large amount of data. Aiming at realizing high accuracy EVs load prediction results utilizing scarce data, a data generation approach in terms of a generative adversarial network (GAN) is put forward. This method can generate high quality data from the scarce initial data to enhance prediction precision. Additionally, the prediction framework is established on the basis of the multi-kernel extreme learning machine (MKELM) combining poly and RBF kernel functions, parameters of which are optimally chosen via the grasshopper optimization algorithm (GOA). Through comparing with the five comparison models and three data generation methods, the validity and feasibility of the established EVs load prediction framework is verified. © 2024 IEEE.

关键词： Data generation EV load prediction Generative adversarial networks grasshopper optimization algorithm Multi-kernel extreme learning machine

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Optimal Harmonic Estimation in Renewable Integrated Power System Using grasshopper Optimisation algorithm and Recursive Least Square Approach

Optimal Harmonic Estimation in Renewable Integrated Power Sy...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Bhoi, Ashok Mallick, Ranjan Kumar Nayak, Pravati Mishra, Sairam Panda, Gayadhar Gantyat, Amaresh National Institute of Technology Electrical Engineering Meghalay Shillong India Siksha 'O' Anushandhan deemed to be University Bhubaneswar India Siksha 'O' Anushandhan deemed to be University Bhubaneswar India

ISBN: (纸本)9798350350821

Harmonic estimation in renewable integrated power systems is crucial for maintaining power quality and ensuring the reliability of the grid. This paper presents an innovative approach for optimal harmonic estimation by combining the grasshopper optimization algorithm (GOA) with the Recursive Least Square (RLS) method. RLS is used for estimation of amplitude of harmonics, whereas phase is estimated by GOA. The performance of GOA-RLS is investigated in detail for estimation of power system signals using test signals buried with noise. The integration of GOA, a nature-inspired optimization technique, with RLS, a statistical method, provides a robust framework for accurately identifying harmonics in power systems. The proposed method is tested on a simulated renewable integrated power system, demonstrating superior performance in terms of accuracy, convergence speed. The results highlight the potential of this hybrid approach in enhancing the operational efficiency and stability of modern power grids with high penetration of renewable energy sources. © 2024 IEEE.

关键词： grasshopper optimization algorithm Harmonic Estimation Recursive Least square approach

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Multi-objective planning of micro-grid system considering renewable energy and hydrogen storage systems with demand response

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International Journal of Hydrogen Energy 2023年第41期48卷 15626-15645页

作者： Zhu, J.R. Jin, Yihua Zhu, Weihong Lee, Dong-Kun Bohlooli, Navid Interdisciplinary Program in Landscape Architecture Seoul National University South Korea College of Agriculture Yanbian University JiLin China College of Geography and Ocean Sciences Yanbian University JiLin China Department of Landscape Architecture and Rural System Engineering Seoul National University Seoul South Korea Electrical Engineering Department Sun-Life Company Baku Seoul Azerbaijan

Recently, researchers in the power system industry have considered the intelligent parking lot idea. The intelligent parking lots (IPL) try to exchange power with the upstream by electric vehicles (EVs) charge/discharge application. The mentioned process of charge/discharge can provide a solution model of regular problems in this field, i.e., peak time problems. Additionally, such vehicles' existence can play an essential role in environmental performance enhancement in power systems. The primary purpose of this paper is to consider such enhancements of EVs in power systems and environmental performance. For this purpose, a multi-objective optimization approach is suggested for the environmental performance and economical operation of IPL by considering the time-of-use rates of a demand response program. Since such a model is associated with various practical bounds, the multi-objective grasshopper optimization algorithm is suggested to solve this problem. The outcomes indicate the effectiveness of the compared techniques utilizing fuzzy decision-making strategies. Chaos theory is employed to increase this method and progress searching operators. Additionally, the proposed multi-objective technique is a model created using fuzzy theory, variable detection, strategy selection-based memory, and non-dominated sorting theory to choose the best Pareto from a list of solutions with robust functionality for resolving the problems mentioned above. The proposed technique is tested on a sample system that includes an intelligent parking lot, local production facilities, and non-renewable and renewable production systems under the proposed uncertainty strategy of upstream grid pricing. The study showed the effectiveness of the suggested method. The simulation findings indicate that the emission rate and overall cost of intelligent parking lots are reduced by 3.87 and 2%. It indicates that economic and environmental objectives are also satisfied due to the successful deployment

关键词： Demand side management grasshopper optimization algorithm Intelligent parking lot Multi-objective optimization

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Frequency Control in an Autonomous Two-area Hybrid Microgrid using grasshopper optimization based Robust PID Controller

Frequency Control in an Autonomous Two-area Hybrid Microgrid...

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IEEE India International Conference on Power Electronics

作者： Anil Annamraju Srikanth Nandiraju Department of EE NITW Warangal India

ISBN: (纸本)9781538649978;9781538649961

The intermittency in the renewable energy sources output power, along with the system and load perturbations in the standalone multi-microgrid results in large frequency deviations. The traditional PI/PID controller is unable to provide the acceptable performance for the operating conditions as mentioned above. To address this problem, this paper suggests an efficient load frequency controller for the frequency control of a microgrid (MG). In this proposed controller, the parameters of PID controllers are fine-tuned by using grasshopper optimization algorithm (GOA). In the proposed strategy, diesel engine generators (DEGs) and redox flow batteries (RFBs) are responsible for balancing the generation and load in the hybrid microgrid system. The proposed controller is executed on an autonomous 2-area hybrid microgrid test system, under various parametric and systemic disturbances. Finally, the superiority of the proposed controller is alleviated concerning error reduction, settling time and overshoot by comparing with various techniques available in the literature.

关键词： Frequency control Robust controller Two-area microgrid RFBs grasshopper optimization algorithm

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