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grasshopper optimization algorithm-Based PI Controller Scheme for Performance Enhancement of a Grid-Connected Wind Generator

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2020年第2期31卷 393-401页

作者： Amin, Mina N. Soliman, Mahmoud A. Hasanien, Hany M. Abdelaziz, Almoataz Y. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Petr Marine Serv Co Dynam Positioning & Nav Dept Alexandria 22776 Egypt Future Univ Egypt Fac Engn & Technol Cairo Egypt

Wind power installation has been rapidly increasing around the globe, which resulted in a massive penetration into the electric grids. So, tremendous attempts are exerted to improve the behaviour of the grid-tied permanent magnet synchronous generator driven by a variable-speed wind turbine (PMSG-VSWT). This study exhibits the application of the grasshopper optimization algorithm-based proportional-integral (PI) controller with the purpose of enhancing the dynamic and transient stability performances of the grid-tied PMSG-VSWT. The simulation-based optimization method is utilized in the optimization process, where the integration of square error criterion is considered as a fitness function. The effectiveness of the proposed control technique is compared with Newton-Raphson algorithm-based PI controllers, taking into account subjecting the system to symmetrical and unsymmetrical fault conditions. For achieving practical responses, real wind speed data that extracted from Zafarana wind farm in Egypt are utilized in this study. The feasibility of the proposed control approach is validated by the simulation studies, which are accomplished by using MATLAB/Simulink environment.

关键词： Frequency converter Permanent-magnet synchronous generator Variable-speed wind turbine grasshopper optimization algorithm

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grasshopper optimization algorithm for Automatic Voltage Regulator System 5

Grasshopper Optimization Algorithm for Automatic Voltage Reg...

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5th International Conference on Electrical and Electronics Engineering (ICEEE)

作者： Hekimoglu, Baran Ekinci, Serdar Batman Univ Dept Elect & Elect Engn Batman Turkey Batman Univ Dept Comp Engn Batman Turkey

ISBN: (纸本)9781538663929

A novel design method is presented to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system utilizing the grasshopper optimization algorithm (GOA). The proposed approach is a simple and effective algorithm that is able to solve many optimization problems even those with unknown search spaces effectively. The simplicity of algorithm provides high quality tuning of optimal PID controller parameters. The integral of time weighted squared error (ITSE) is used as the performance index to confirm the performance of the proposed GOA-PID controller. When compared to the other PID controllers based on Ziegler-Nichols (ZN), differential evolution (DE), and artificial bee colony (ABC) tuning methods, the proposed method is found highly effective and robust to improve AVR system's transient response.

关键词： automatic voltage regulator grasshopper optimization algorithm parameter tuning

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grasshopper optimization algorithm based Fuzzy PD-PI Cascade Controller for LFC of Interconnected Power System Coordinate with Renewable sources 6

Grasshopper Optimization Algorithm based Fuzzy PD-PI Cascade...

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6th IEEE Calcutta Conference (CALCON)

作者： Tripathy, Debasis Choudhury, N. B. Dev Behera, Sadasiva Sahu, Binod Kumar NIT Silchar Dept Elect Engn Silchar 788010 Assam India Siksha O Anusandhan Univ Dept Elect Engn Bhubaneswar 751030 India

ISBN: (纸本)9781728142838

The frequency instability issue in an interconnected power system due to small disturbances can be overcome by the load frequency control (LFC) scheme using a suitable secondary controller. So, this article introduces a grasshopper optimization algorithm (GOA) based fuzzy PD-PI cascade controller for the LFC study considering a three area interconnected system. Initially, each area of the interconnected power system comprising of a thermal generating unit and further to show the impact of renewable energy sources ( RES), a combination of wind generating unit and the solar-thermal generating unit is incorporated along with thermal generating unit in area-1. The study begins with the performance verification of the GOA technique over other techniques. Then to get sure about the selection of objective function, the performance of the GOA-PI controller has been verified by considering two mostly used objective functions i.e. integral time multiplied absolute error and integral square error. Lastly, the performances of the proposed controller have been verified over other controllers for the system with/without incorporating RES.

关键词： fuzzy logic controller grasshopper optimization algorithm load frequency control renewable energy sources

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grasshopper optimization algorithm for Optimal Sizing of a Stand-Alone Hybrid Energy System 16

Grasshopper Optimization Algorithm for Optimal Sizing of a S...

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16th International Conference on Compatibility, Power Electronics, and Power Engineering (CPE-POWERENG)

作者： Al-Shamma'a, Abdullrahman A. Farh, Hassan M. H. Alkuhayli, Abdulaziz A. Noman, Abdullah M. Abdurraqeeb, Akram M. King Saud Univ Coll Engn Elect Engn Dept Riyadh Saudi Arabia

ISBN: (纸本)9781665496780

Hybrid energy systems (HES) that combine Photovoltaic (PV) arrays with Wind turbines (WTs) are excellent options for powering rural locations. The optimal design of HESs with distributed generations (DGs) (i.e., PV, WT) is still a hot topic due to the uncertainties in solar irradiance and wind speed. Based on a long-term cost analysis, this paper formulates an application of grasshopper optimization algorithm (GOA) in the field of HES design problem. GOA's efficacy is studied, and its performance is compared to that of the genetic algorithm (GA), butterfly optimization algorithm (BOA), and big-bang-big-crunch (BBBC) algorithm. The GOA outperformed the other seven metaheuristic algorithms where it attained the optimal solution of the HES with the lowest ASC. It can be revealed that the GOA has acceptable performance in terms of the global solution capture with less oscillations and the convergence rate.

关键词： Hybrid energy systems (HES) grasshopper optimization algorithm Optimal design Wind turbines Photovoltaic

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grasshopper optimization algorithm based improved variational mode decomposition technique for muscle artifact removal in ECG using dynamic time warping

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期73卷 103437-103437页

作者： Malghan, Pavan G. Hota, Malaya Kumar Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore 632014 Tamil Nadu India

The removal of unwanted noise from electrocardiogram (ECG) recordings is a difficult procedure in biomedical signal analysis. It alters the signal and affects the accurate interpretation of the signal. A major distortion produced while recording is due to muscle artifact (MA) or electromyographic (EMG) noise. To achieve a high quality ECG recording by eliminating this MA noise, our research proposes a novel filtering technique using the grasshopper optimization algorithm (GOA) based variational mode decomposition (VMD) method with the dynamic time warping (DTW) distance concept. GOA is utilized to identify the best optimal parameters for the VMD process, and then the DTW technique is applied to discover the relevant modes that include MA noise. To successfully remove noise, these modes are denoised using the discrete wavelet transform (DWT) method. ECG signal analysis is performed on a real-time MIT-BIH arrhythmia database and is compared with the performance of recent existing techniques using signal characteristics such as signal-to-noise ratio (SNR), mean square error (MSE), correlation coefficient (CC), and so on. As an outcome of these computations, our proposed technique outperforms those existing methods when it comes to denoising MA noise.

关键词： grasshopper optimization algorithm Variational mode decomposition Dynamic time warping Discrete wavelet transform Muscle artifact

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LAGOA: Learning automata based grasshopper optimization algorithm for feature selection in disease datasets

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第6期13卷 3175-3194页

作者： Dey, Chiradeep Bose, Rajarshi Ghosh, Kushal Kanti Malakar, Samir Sarkar, Ram Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Jadavpur Univ Dept Comp Sci & Engn Kolkata India Asutosh Coll Dept Comp Sci Kolkata India

In predictive modelling it is important to use any feature selection methods as irrelevant features when used with powerful classifiers can lead to over-fitting and thus create models which fail to perform as good as when these features are not used. Particularly it is important in case of disease datasets where various features or attributes are available through the patients' medical records and many features in these datasets may not be relevant to the diagnosis of some specific disease. Wrong models in this case can be disastrous and lead to wrong diagnosis, and maybe in extreme cases lead to loss of life. To this end, we have used a wrapper based feature selection model for the said purpose. In recent years, grasshopper optimization algorithm (GOA) has proved its superiority over other optimization algorithms in different research areas. In this paper, we propose an improved version of GOA, called (LAGOA), which uses Learning Automata (LA) for adjusting the parameters of GOA in an adaptive way, and two-phase mutation for enhancing exploitation capability of the algorithm. LA is used for adjusting the parameter values of each grasshopper in the population individually. In two-phase mutation the first phase reduces the number of selected features while preserving high classification accuracy, while the second phase adds relevant features which increase the classification accuracy. Proposed method has been applied to Breast Cancer (Wisconsin), Breast Cancer (Diagnosis), Statlog (Heart), Lung Cancer, SpectF Heart and Hepatitis datasets taken from UCI Machine Learning Repository. Experimental results confirm its superiority over state-of-the-art methods considered here for comparison.

关键词： grasshopper optimization algorithm Learning automata Two-phase mutation Biomedical data Feature selection Cancer data

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Optimal design of CDM controller to frequency control of a realistic power system equipped with storage devices using grasshopper optimization algorithm

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ISA TRANSACTIONS 2020年第0期97卷 202-215页

作者： Heshmati, Mina Noroozian, Reza Jalilzadeh, Saeid Shayeghi, Hossein Univ Zanjan Dept Elect Engn Zanjan Iran Univ Mohaghegh Ardabili Dept Elect Engn Ardebil Iran

This paper presents a new robust load frequency control (LFC) technique based on an optimal design of the coefficient diagram method (CDM) in a three area thermal power system equipped with redox flow batteries (RFB). In order to emphasize on a realistic power system and obtain an accurate insight, important nonlinearities due to generation rate constraint (GRC), governor dead band (GDB) and time delay (TD) were considered. The innovation of the proposed controller in this paper is the use of a hybrid intelligent combination of a decentralized CDM technique and optimization throughout its algebraic equations. In addition, a new algorithm namely grasshopper optimization algorithm (GOA) was used to find the key parameters of the proposed controller in solving the LFC problem for the first time. Furthermore, this study applied a powerful modified objective function by considering the integral of time multiplied squared error (ITSE) criteria for both controller input signal (ACE), to minimize the area control error and output signal (Delta u) to reduce the size of the actuator, settling time (Ts) to have a faster response and a function to increase the minimum damping ratio (MDR) among all eigen values. To demonstrate the effectiveness of the proposed scheme, the studied simulated power system was tested through different cases including a large step and sinusoidal load perturbations and wide uncertainty in dynamic parameters of a nonlinear power system. Comparative results have revealed the superiority of the optimal CDM technique, especially when it is equipped with RFB. In addition to graphical results, by taking into account the MDR of different control strategies, the preference of the proposed controller has become more validated. This newly developed strategy leads to a flexible and accurate controller with a powerful mathematical back up which can successfully cope with GRC, GDB and TD nonlinearities in perturbed uncertain power systems and provide fast, stable

关键词： Optimal coefficient diagram method RFB LFC grasshopper optimization algorithm GRC GDB Time delay

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A grasshopper optimization algorithm for optimal short-term hydrothermal scheduling

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ENERGY REPORTS 2021年 7卷 314-323页

作者： Zeng, Xie Hammid, Ali Thaeer Kumar, Nallapaneni Manoj Subramaniam, Umashankar Almakhles, Dhafer J. Baoji Univ Arts & Sci Sch Comp Sci Baoji 721000 Peoples R China Univ Pendidikan Sultan Idris Fac Arts Comp & Creat Ind Dept Comp Tanjong Malim Malaysia Imam Jaafar Al Sadiq Univ Fac Informat Technol Comp Engn Tech Dept Baghdad Iraq City Univ Hong Kong Sch Energy & Environm Kowloon Hong Kong Peoples R China Prince Sultan Univ Coll Engn Dept Commun & Networks Renewable Energy Lab Riyadh 12435 Saudi Arabia

The optimal generation for short-term hydrothermal scheduling (OGStHS) with the deliberation of various purposes is a complex non-linear constrained optimization problem. There exist numerous constraints, which make the OGStHS optimization problem more complicated. The considered constraints for this problem are mostly related to energy performance, operational conditions, water, and power infrastructure. All these constraints would generally influence the cost of fuel. In this study, a multi-objective optimization form of OGStHS is suggested to estimate the minimum cost of fuel, which mainly influences industrial operation. The water transfer delays among multi-related reservoirs and the thermal plants' valve-point influences are considered for the accurate formulation of the OGStHS problem. Meantime, a grasshopper optimization algorithm (GOA) is performed to handle the OGStHS problem by getting optimized for both objectives concurrently. A modern approach is shown in this study to get a solution to the OGStHS problem. Furthermore, to deal with the complex restraints efficiently, modern heuristic restriction treatment processes with no drawback impact frames have been offered in this study. Two hydrothermal power systems have illustrated the suggested GOA technique's utility and performance. Compared with other available approaches, the analytical results are admitted that GOA can provide a better understanding by decreasing fuel cost and emission concurrently. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Thermal power plants Co-ordinated hydrothermal Hydrothermal constraint Water transfer delays Valve-point influences grasshopper optimization algorithm

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A novel quantum grasshopper optimization algorithm for feature selection

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2020年 127卷 33-53页

作者： Wang, Dong Chen, Hongmei Li, Tianrui Wan, Jihong Huang, Yanyong Southwest Jiaotong Univ Sch Informat Sci & Technol Inst Artificial Intelligence Chengdu 611756 Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu 611130 Peoples R China

Feature selection is an indispensable work to make the data mining more effective. It reduces the computational complexity and effectively improves the performance of learning models. The exhaustive algorithm and the greedy algorithm cannot adapt to the current increasing number of features when finding the potential optimal feature subset. Therefore, the feasible way for feature selection called swarm intelligence algorithms becomes popular. The grasshopper optimization algorithm is a novel swarm intelligence algorithm which has good performance. In this study, we improve the grasshopper optimization algorithm by applying quantum method. A dynamic population quantum binary grasshopper optimization algorithm based on mutual information and rough set theory (DQBGOA_MR) for feature selection is proposed. Through the quantization of grasshopper individuals, the search scope of feature space is improved, and a good balance is achieved between exploration and exploitation. The premature and catastrophe strategies are used to avoid converge prematurely and fall into a local optimum. Moreover, the rough set and mutual information based evaluation criterion is defined which considers both effectiveness of selected features and the relation among unselected features, selected features, and classes. The proposed method is evaluated by extensive experiments in twenty UCI datasets. Experimental results show that DQBGOA_MR has better feature reduction rate, higher classification accuracy and more stable results compared to other swarm intelligence algorithms. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Feature selection grasshopper optimization algorithm Quantum computation Rough sets Mutual information

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Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

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ENGINEERING WITH COMPUTERS 2022年第4期38卷 3341-3350页

作者： Bayat, Parichehr Monjezi, Masoud Mehrdanesh, Amirhossein Khandelwal, Manoj Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Federat Univ Australia Sch Engn Informat Technol & Phys Sci Ballarat Vic 3350 Australia

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

关键词： Ground vibration Meta-heuristic optimization Gene expression programming grasshopper optimization algorithm

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