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grasshopper optimisation algorithm of multistage PDF

INTERNATIONAL JOURNAL OF AMBIENT ENERGY 2020年第1期43卷 1469-1481页

作者： Nayak, Pratap Chandra Prusty, Ramesh Chandra Panda, Sidhartha VSSUT Dept Elect Engn Burla India

This article introduces grasshopper optimisation algorithm (GOA)-based multistage Proportional Derivative filter type (PDF) with One plus Proportional Integral (1 + PI) controller for Automatic Generation control (AGC) in dispersed type three area power system. The control-area 1 consists of thermal, hydro and Wind units. Similarly, control-area 2 consists of thermal, hydro and Diesel units. Control-area 3 consists of thermal, hydro and Gas units. Nonlinearities like Governor dead-band, GRC (generation rate-constraint) and boiler dynamics are included for realistic representation. A comparative analysis is carried out by GOA with multistage PDF + (1 + PI) controller with classical PID/PI controller to justify the superiority of the proposed controller. The dominance characteristic of GOA with PDF + (1 + PI) controller structure is tested over Particle swarm optimisation (PSO) and Genetic algorithm (GA) optimisers with PDF + (1 + PI) controller. The robustness of the complete framework was justified by study and analysis of sensitivity by enhancing and decaying SLP and system elements in a band 50% from its minimal value. From the analysis, it is found that the GOA method with PDF + (1 + PI) controller is providing dominance characteristics performance with random load variations and system parameters of the various components of the power system.

关键词： Automatic generation control boiler dynamics generation rate constraint governor dead band grasshopper optimisation algorithm multistage PDF + (1 + PI) controller

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A comprehensive study of chaos embedded bridging mechanisms and crossover operators for grasshopper optimisation algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期132卷 166-188页

作者： Saxena, Akash Swami Keshvanand Inst Technol Dept Elect Engn Management & Gramothan Jaipur Rajasthan India

In recent years, the trend of embedding chaos in the optimization algorithms has grown multifold. Usually, the chaotic algorithms employ chaotic sequences instead of random numbers, in the exploration phase or they employ chaotic numbers for decision making in the exploitation phase. In literature, the positive impact of chaos over the performance of algorithms have been studied and reported. However, very little work is reported on the implications of chaos on the bridging mechanism between the exploration and exploitation phases. This paper presents implications of different chaotic sequences on the performance of grasshopper optimisation algorithm (GOA). A bridging mechanism based on sinusoidal truncated function is proposed first, then 10 different normalize chaotic sequences are employed with this function. Along with this bridging mechanism, a chaotic operator derived crossover scheme is also proposed. These experiments evolve 11 different variants of GOA. The proposed modifications maintain an effective balance between exploration and exploitation phases. Simultaneously, it reduces the attraction, repulsion and comfort zone chaotically. New variants are benchmarked on latest 29 Congress on Evolutionary Computation-2017 (CEC-2017) functions. Efficacy of these variants is validated with several statistical tests and plots. Further, some real life challenging problem of engineering domain are considered for evaluating the efficacy of the proposed variants. These problems are Model Order Reduction (MOR) of control engineering, Protein Structure Prediction (PSP) of bio informatics and Frequency Modulated Sound Wave Parameter Synthesis Problem of parameter estimation. Results reveal that proposed variants exhibit better exploration and exploitation properties as compared to the parent algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： grasshopper optimisation algorithm Crossover Congress on Evolutionary Computation (CEC) Chaos theory

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Optimal allocation and control of fixed and switched capacitor banks on distribution systems using grasshopper optimisation algorithm with power loss sensitivity and rough set theory

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第17期13卷 3863-3878页

作者： Elsayed, Abdullah M. Mishref, Mohammed M. Farrag, Sobhy M. Damietta Univ Dept Elect Engn Fac Engn New Damietta 34517 Egypt Menoufia Univ Dept Elect Engn Fac Engn Shibin Al Kawm 32511 Egypt Middle Delta Elect Prod Co Elatf Power Stn Mahmoudia Egypt

The grasshopper heuristic optimisation algorithm (GOA) is one of the newest heuristic techniques. It attempts to imitate locust's behaviour in solving different problems. In this paper, first, optimal location and size of fixed capacitor banks on distribution systems have been obtained using the GOA. Determination of the optimal number of capacitor banks and their optimal locations and sizes represent one of the major challenges facing distribution system operators. Therefore, second, a combined power loss sensitivity and GOA technique has been introduced to obtain the optimal number, location and size of the capacitor banks on distribution systems. Third, a proposed technique for optimal placement of fixed and switched capacitor banks considering daily load variations is introduced. Finally, a new technique of combined rough set theory and GOA is proposed to minimise daily switching of the capacitor banks and minimise daily power losses. Four test systems with different sizes and complexities are considered to evaluate the proposed techniques which are the 33, 69, 85 and 141-bus systems. To clarify the validation and effectiveness of the proposed solution techniques, the obtained results have been compared with other previously used solving techniques. The obtained results demonstrate the accuracy and effectiveness of the introduced techniques.

关键词： optimisation capacitor switching power capacitors rough set theory reactive power control distribution networks genetic algorithms distributed power generation power distribution planning newest heuristic techniques optimal location size fixed capacitor banks grasshopper new heuristic optimisation technique optimal number optimal locations sizes distribution system operators large-scale distribution systems combined power loss sensitivity GOA technique optimal placement fixed switched capacitor banks combined rough set theory daily power losses distribution system performance increases capacitors test systems 141-bus systems solving techniques introduced techniques control grasshopper optimisation algorithm grasshopper heuristic optimisation algorithm

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Expeditious frequency control of solar photovoltaic/biogas/biodiesel generator based isolated renewable microgrid using grasshopper optimisation algorithm

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IET RENEWABLE POWER GENERATION 2018年第14期12卷 1659-1667页

作者： Barik, Amar Kumar Das, Dulal Chandra NIT Silchar Dept Elect Engn Silchar India

This work endeavours to propose an approximate mathematical model of a community based renewable microgrid with solar photovoltaic, biogas and biodiesel generators including battery storage for load frequency studies. It becomes a great challenge to coordinate between generation and load demand of the microgrid as the renewable sources are highly unpredictable and nature dependent. To overcome this issue, the responses of the system are studied under different real-world scenarios of renewable source availabilities and load variations with a maiden approach towards optimising the controller gains using a recent grasshopper optimisation algorithm (GOA) for efficient frequency control. The frequency responses of proposed microgrid are compared with different conventional controllers and some popular optimisation algorithms using MATLAB/Simulink. Finally, proportional-integral-derivative controller with GOA is preferred for the case studies under four cases of source variations with step load perturbation and one case of simultaneous source and load variations. The results of all these five scenarios are found satisfactory in terms of frequency responses and reported in the work.

关键词： distributed power generation power generation control optimisation solar power stations photovoltaic power systems frequency control biofuel diesel-electric generators three-term control expeditious frequency control solar photovoltaic based isolated renewable microgrid biogas based isolated renewable microgrid biodiesel generator based isolated renewable microgrid grasshopper optimisation algorithm approximate mathematical model community based renewable microgrid battery storage load demand load frequency studies renewable source availabilities load variations controller gain optimisation GOA MATLAB-Simulink proportional-integral-derivative source variations step load perturbation frequency responses

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Hourly load shifting approach for demand side management in smart grid using grasshopper optimisation algorithm

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第5期14卷 808-815页

作者： Jamil, Majid Mittal, Sonam Jamia Millia Islamia Dept Elect Engn New Delhi India

In this new era of communication, the advent of the smart grid has revolutionised the power system network. The goal of smart grids is to provide a more reliable, environment-friendly and economically efficient power system. Demand side management or demand side response is one of the key components of the smart grid which accomplishes the smart grid that would provide intelligence to the traditional grid. Here, a new approach has been proposed for the demand side management, which is based on shifting a load from peak to off-peak time. The main objective of the work is to reduce the peak hour demand and the utility bill of the consumers. To achieve these objectives, the proposed strategy is modelled as a minimised optimisation problem and it tries to find out the optimal solution. For that, two optimisation algorithms, the first one is particle swarm optimisation algorithm and the second one is grasshopper optimisation algorithm, are proposed and applied in three area loads of the smart grid, i.e. residential, commercial and industrial. The obtained simulation results show a significant reduction in peak hour demand and utility bills.

关键词： power generation economics optimisation particle swarm optimisation demand side management smart power grids hourly load shifting approach demand side management smart grid grasshopper optimisation algorithm traditional grid peak hour demand

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An improvement of Global Maximum Power Point Tracking Using a Novel grasshopper optimisation algorithm of Photovoltaic System

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2024年第2期48卷 929-943页

作者： Tamilarasan, T. Suganyadevi, M. V. SMR Automot Syst India Ltd Qual Assurance Dept Kanchipuram India Saranathan Coll Engn Dept EEE Trichy India

The performance of solar photovoltaic (PV) panels is entirely determined by ambient temperature, solar irradiance, and dynamic environmental conditions. As a result, the photovoltaic system exhibits multiple peaks in the I-V and P-V curves during partial shading conditions (PSC), which significantly reduces power output. The maximum power point tracking (MPPT) method is essential for extracting maximum power from the PV panel during PSC. With conformist MPPT algorithms, determining the maximum power point is unrealistic. To overcome the constraints, this paper proposes the grasshopper optimisation algorithm (GOA), which imitates the behaviour of grasshopper swarms in nature and is capable of extracting maximum power even during unfavourable shading conditions. The performance assessment of GOA method has been carried out in the MATLAB/SIMULINK environment. This algorithm effectiveness is validated by comparing its performance with conventional and other most prominent global search counterparts. The proposed algorithm is validated in real-time hardware with boost converter through different PV array pattern. The outcome demonstrates the effectiveness of the proposed algorithm which drastically reduces the computation time and performs rapidly and precisely to extract the global maximum peak with minimal oscillations.

关键词： Maximum power point tracking grasshopper optimisation algorithm Boost converter PV panel Computational time

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Multi-objective grasshopper optimisation algorithm applied to Design DNA Sequences 15

Multi-objective Grasshopper Optimisation Algorithm applied t...

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15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Guo, Huaiyu Feng, Zhiyu Wang, Xiao Zhejiang Normal Univ Sch Comp Sci & Math Jinhua 321000 Zhejiang Peoples R China Zhejiang Normal Univ Xingzhi Coll Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9798350321456

High stable and high reliable DNA sequences are requisite in DNA computing search field. DNA sequences can be quantified in an array of thermodynamical and logical constraints which play an important role in process of hybridization. Since designing of DNA sequences set regraded as a multi objective optimization problem due to these constraints, epsilon constraint introduced in this paper which is a thought of reduce object dimensions. These constraints can be classified in single squence constraints and multiple sequences constraints, two meta heuristic algorithms tunicate swarm algorithm and grasshopper optimisation algorithm used in different steps according to their features and applicable scenarios. Result of this progress shows that epsilon constraints collaborate with other algorithms may produce advanced DNA sequences set compare to previous work.

关键词： DNA sequence design DNA computing tunicate swarm algorithm grasshopper optimisation algorithm epsilon constraint

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Predicting the construction labour productivity using artificial neural network and grasshopper optimisation algorithm

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INTERNATIONAL JOURNAL OF CONSTRUCTION MANAGEMENT 2023年第5期23卷 763-779页

作者： Goodarzizad, Payam Mohammadi Golafshani, Emadaldin Arashpour, Mehrdad Islamic Azad Univ Dept Civil Engn Tehran Sci & Res Branch Tehran Iran Monash Univ Dept Civil Engn Melbourne Vic Australia

The construction industry is mainly dependent on human resources, and labour costs are significant. Although many researchers have investigated construction labour productivity (CLP), the lack of adequate studies in this field is evident in developing countries. This paper intends to measure the CLP of the concrete pouring operations related to the construction of commercial-office complex projects in Iran. For this purpose, 19 critical factors with significant impact on the CLP were identified and listed in five groups, including individual, managerial, economic, technical, and environmental aspects. Then, a hybrid model based on artificial neural network (ANN) and grasshopper optimisation algorithm (GOA) was developed to determine the most influential factors and increase the CLP model's precision. Data related to the CLP of 24 under-construction commercial-office complex projects in Iran were gathered. Results reveal the most influencing factors on the CLP are labour experience and skill and motivation of labour from the individual group, the amount of pay from the economic group, site accidents from the technical group, proper supervision from the management group, and weather conditions from the environmental group. The findings can facilitate the development of more efficient project schedules, increasing the CLP, and reducing project costs.

关键词： Construction labour productivity artificial neural network grasshopper optimisation algorithm

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Harmonics Minimization in MLI Using grasshopper Optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRONICS 2022年第6期109卷 1009-1034页

作者： Chew, W. T. Yong, W. V. Ong, S. L. Leong, J. H. Univ Malaysia Perlis Sch Elect Syst Engn Pauh Putra Campus Arau 02600 Perlis Malaysia Univ Malaysia Perlis Sch Microelect Engn Arau Malaysia

This paper proposes the use of a nature-inspired algorithm known as grasshopper optimisation algorithm for optimising switching angles applied to a three-phase 7-level cascaded H-bridge multilevel inverter. Switching angles obtained using the algorithm can be used to synthesise an optimised output voltage waveform with desired fundamental component and minimised 5th and 7th order harmonics. Simulation results show that the algorithm is able to provide switching angles over a wider modulation index range than Newton Raphson technique and is capable of minimising the objective function better than Genetic algorithm. MATLAB/Simulink-PSIM dynamic co-simulation performed on a three-phase 7-level cascaded H-bridge multilevel inverter operated using GOA derived switching angles is able to respond to varying modulation index demand and produce the output voltage waveform with the desired fundamental component and minimised selected 5th and 7th order harmonics. Both simulation and experimental results obtained from the three-phase 7-level cascaded H-bridge multilevel inverter show good agreement, confirming the validity of the switching angles obtained using the grasshopper optimisation algorithm.

关键词： Cascaded H-bridge multilevel inverter selective harmonic minimisation pulse width modulation total harmonic distortion grasshopper optimisation algorithm

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grasshopper optimisation based robust power/frequency regulator for shipboard micro-grid

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IET RENEWABLE POWER GENERATION 2020年第17期14卷 3568-3577页

作者： Choudhary, Atul Kumar Prakash, Surya Sharma, Mandeep Dhundhara, Sandeep Thapar Inst Engn & Technol Elect & Instrumentat Engn Dept Patiala Punjab India Baba Hira Singh Bhattal Inst Engn & Technol Dept Elect Engn Lehragaga Punjab India CCS Haryana Agr Univ Coll Agr Engn & Technol Dept Basic Engn Hisar Haryana India

Due to the rapid increase in electrical energy requirements in marine power systems (MPSs), and to reduce the consumption of fossil fuel, there is an emergent need to utilise renewable energy sources (RESs) in MPSs, which has been an attractive field of research. This research aims to present a novel load frequency control (LFC) scheme for a shipboard micro-grid (SMG) system. Therefore, a MPS with photovoltaic, wind turbine, hybrid energy storage system (ESS), and diesel generator (DG) have been simulated to relate an exact mobile islanded SMG. The system is designed using the transfer function models with the above said generating units and storage systems. grasshopper optimisation algorithm (GOA) tuned fuzzy-based proportional-integral-derivative with filter control technique has been proposed to investigate the performance of the LFC scheme for the proposed SMG system. GOA and particle swarm optimisation optimised controllers have been designed and performance evaluation has been carried out on the conventional, adaptive neuro-fuzzy inference system, and fuzzy cascaded with a conventional controller. The responses obtained from the simulations for different cases are analysed to justify the novelty and superiority of the proposed technique for frequency and power regulation.

关键词： frequency control wind turbines three-term control load regulation particle swarm optimisation hybrid power systems diesel-electric generators robust control fuzzy control power generation control distributed power generation fuzzy reasoning marine power systems power distribution control photovoltaic power systems electrical energy requirements marine power systems MPS fossil fuel renewable energy sources load frequency control scheme shipboard microgrid system photovoltaic-wind turbine hybrid energy storage system diesel generator transfer function models grasshopper optimisation algorithm GOA filter control technique LFC scheme particle swarm optimisation adaptive neuro-fuzzy inference system power regulation SBM system grasshopper optimisation robust power-frequency regulator fuzzy-based proportional-integral-derivative frequency regulation mobile islanded SBM

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