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Electronic Load Controller based on modified firefly algorithm to Reduce Frequency Fluctuation of Generator in Micro Hydro Power Plants

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2023年第2期13卷 601-611页

作者： Iksan, Nur Firdaus, Muhammad Ubaid Subiyanto Apriaskar, Esa Nugroho, Anan Udayanti, Erika Devi Widodo, Djoko Adi Semarang State Univ Fac Engn Dept Elect Engn Semarang Indonesia Dian Nuswantoro Univ Fac Comp Sci Dept Informat Syst Semarang Indonesia State Univ Semarang Fac Engn Dept Elect Engn Bldg E11 Semarang City 50229 Central Java Pr Indonesia

An overpowering condition generated by the PLMTH generator simulator will cause frequency instability which can result in some damage to electronic goods. So, it is necessary to design an optimal control system. One of the control systems used is the Electronic Load Controller (ELC) which can adjust the excess power to consumer loads and ballast loads. Development of an ELC that uses an electrical switch in the form of a Triac. This component will be controlled by a microcontroller with input data in the form of voltage, current, and frequency values. In the processing of microcontroller data, algorithms can be applied to obtain more optimal ELC performance. One of them is using the modified firefly algorithm (MFA). With this method, a PID tuning approach is taken so that it will get PID parameters on the Electronic Load Controller (ELC) system. In this MFA tuning process, up to 100 iterations are repeated to achieve optimal or near-optimal PID parameters. The results of this study were to measure the performance of the MFA ELC which was tested with a PLTMH generator simulator. Based on the test results, shows that the performance of the MFA ELC can reduce frequency fluctuations so that the frequency value is more stable within the standard limits set by the Ministry of Energy and Mineral Resources of the Republic of Indonesia of 50.1Hz - 51.5 Hz. In addition, the generated voltage also becomes more stable, namely in the range of 222.2 - 223.6 Volts. These results when compared with testing the generator without using the MFA ELC are very different, because the frequency values produced are in the interval 46.7 - 51.5 Hz and the voltage values are in the interval 214.2 - 244.5 Volts. Thus, the performance of the MFA ELC is sufficient to help reduce frequency instability in the PLTMH generator simulator.

关键词： Electronic Load Controller (ELC) Frequency Fluctuation modified firefly algorithm PID Controller

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A modified firefly algorithm applying on multi-objective radial-based function for blasting

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NEURAL COMPUTING & APPLICATIONS 2022年第3期34卷 2455-2471页

作者： Abbaszadeh Shahri, Abbas Khorsand Zak, Mohammad Abbaszadeh Shahri, Hossein Islamic Azad Univ Roudehen Branch Dept Civil Engn Tehran Iran Islamic Azad Univ Aligudarz Branch Dept Appl Math Tehran Iran Texas Tech Univ Dept Civil Engn Lubbock TX 79409 USA

Modifying the metaheuristics as a striking alternative of basic algorithms is outstanding and efficient scientific approach in optimization of engineering problems to improve robustness and convergence rate. firefly algorithm (FA) is one of the new metaheuristics inspired by the flashing behavior of fireflies, where the performance of each randomly generated solution on objective function is evaluated by the brightness. In the current paper, a modified firefly algorithm (MFA) was introduced using expectation value and generalized weighted average of a random brightness and then evaluated with different benchmark functions. Since brightness varies with movements of fireflies, the parameter settings can adaptively be tuned for different problems. The capability of the MFA then in hybridizing with a developed automated multi-objective radial-based function network (MORBF) was examined. In blasting engineering, multi-objective models covering the peak particle velocity (PPV) and the vibration frequency (F-vib) due to providing more insight on safety criteria significantly are essential and great of interested. The hybrid MORBF-MFA then was applied on 78 blasting data comprising stemming, burden, spacing, total charge, distance, and charge per delay to provide more accurate predictive model. Detailed executed analyses through different metrics showed 1.01% and 2.43% improvement in hybrid MORBF-MFA corresponding to PPV and F-vib over MORBF-FA. The observed results approved that the introduced MFA as a reliable and feasible tool with accurate enough response can effectively be applied to multi-objective problems. Implemented sensitivity analyses scored the distance and burden as the most and least influences factors on predicted outputs.

关键词： Blasting Vibration frequency Multi-objective model modified firefly algorithm Sensitivity Prediction

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Application of Dynamically Search Space Squeezed modified firefly algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems

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IEEE ACCESS 2021年 9卷 1918-1939页

作者： Liaquat, Sheroze Fakhar, Muhammad Salman Kashif, Syed Abdul Rahman Rasool, Akhtar Saleem, Omer Zia, Muhammad Fahad Padmanaban, Sanjeevikumar Natl Univ Comp & Emerging Sci Dept Elect Engn Lahore 54000 Pakistan Univ Engn & Technol Dept Elect Engn Lahore 54000 Pakistan Sharif Coll Engn & Technol Dept Elect Engn Lahore 55150 Pakistan Aalborg Univ Dept Energy Technol DK-6700 Esbjerg Denmark

The absence of the global best component in the update equation of the conventional firefly algorithm degrades its exploration properties. This research proposes multi-update position criteria to enhance the exploration properties of the conventional firefly technique while including the effect of the global best solution on the movement of the fireflies in the search space of the objective function. Moreover, the dynamic search space squeezing is applied to constrict the movement of the fireflies within the certain limits to avoid their oscillatory movement as the solution approaches towards the global best by determining the optimal trajectory for each firefly. The robustness of the suggested firefly algorithm is tested on a hybrid energy system consisting of thermal, hydroelectric, and Photovoltaic (PV) energy source. The intermittent nature of the PV energy source is explained using fractional integral polynomial model and Auto Regressive Integrated Moving Average (ARIMA) model. The main dispatch problem is successfully computed using both the modified firefly and the simple firefly algorithm by determining the optimal power share of each energy source for different scheduling intervals. The suggested operational strategy reduces the overall generation cost of the system while preserving the various system constraints. Due to the stochastic nature of the meta-heuristic techniques, the two suggested algorithms are compared statistically for different test cases using the independent t-test results. The statistical comparison suggests that the performance of the modified firefly is superior to its conventional counterpart as the evaluation parameters of the modified firefly converge to relatively lower value as compared to the parameters of the simple firefly algorithm.

关键词： modified firefly algorithm auto regressive integrated moving average model firefly algorithm hybrid energy systems independent t-test results

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Probabilistic Structural Model Updating with Modal Flexibility Using a modified firefly algorithm

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MATERIALS 2022年第23期15卷 8630-8630页

作者： Feng, Zhouquan Wang, Wenzan Zhang, Jiren Hunan Univ Coll Civil Engn Key Lab Wind & Bridge Engn Hunan Prov Changsha 410082 Peoples R China Hunan Univ Chongqing Res Inst Chongqing 401151 Peoples R China

Structural model updating is one of the most important steps in structural health monitoring, which can achieve high-precision matching between finite element models and actual engineering structures. In this study, a Bayesian model updating method with modal flexibility was presented, where a modified heuristic optimization algorithm named modified Nelder-Mead firefly algorithm (m-NMFA) was proposed to find the most probable values (MPV) of model parameters for the maximum a posteriori probability (MAP) estimate. The proposed m-NMFA was compared to the original firefly algorithm (FA), the genetic algorithm (GA), and the particle swarm algorithm (PSO) through the numerical illustrative examples of 18 benchmark functions and a twelve-story shear frame model. Then, a six-story shear frame model test was performed to identify the inter-story stiffness of the structure in the original and the damage states, respectively. By comparing the two, the position and extent of damage were accurately found and quantified in a probabilistic manner. In terms of optimization, the proposed m-NMFA was powerful to find the MPVs much faster and more accurately. In the incomplete measurement case, only the m-NMFA achieved target damage identification results. The proposed Bayesian model updating method has the advantages of high precision, fast convergence, and strong robustness in MPV finding and the ability of parameter uncertainty quantification.

关键词： structural health monitoring structural model updating modified firefly algorithm modal flexibility damage detection

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Parameter extraction for small-signal model of GaN HEMTs on SiC substrates based on modified firefly algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2022年第12期32卷 e23518-e23518页

作者： Liu, Yuan Cao, Fengrui Xiong, Xiaoming Huang, Junkai Deng, Wanling Guangdong Univ Technol Sch Integrated Circuits Guangzhou Peoples R China Jinan Univ Guangdong Higher Educ Inst Dept Elect Engn Key Lab New Semicond & Devices Guangzhou 510632 Peoples R China

In this article, an improved firefly algorithm is proposed to extract the parameters of the small signal model of GaN HEMTs on SiC substrates. First, the initial values of the parameters are extracted directly by pinch-off, un-biased cold S-parameters and hot S-parameters. Second, an improved firefly algorithm is proposed to optimize parameters. Finally, in order to verify this method, the performances of traditional algorithm are compared with modified firefly algorithm in convergence speed, execution time, and accuracy, which shows that this proposed method is more suitable for the extraction of high-dimensional parameters model.

关键词： GaN HEMTs on SiC substrates modified firefly algorithm small signal model

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A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

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CONSTRUCTION AND BUILDING MATERIALS 2018年 180卷 320-333页

作者： Dac-Khuong Bui Tuan Nguyen Chou, Jui-Sheng Nguyen-Xuan, H. Tuan Duc Ngo Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sec 4Keelung Rd Taipei 106 Taiwan Chi Minh City Univ Technol Hutech Ctr Interdisciplinary Res Technol Ho Chi Minh City Vietnam

The compressive and tensile strength of high-performance concrete (HPC) is a highly nonlinear function of its constituents. The significance of expert frameworks for predicting the compressive and tensile strength of HPC is greatly distinguished in material technology. This study aims to develop an expert system based on the artificial neural network (ANN) model in association with a modified firefly algorithm (MFA). The ANN model is constructed from experimental data while MFA is used to optimize a set of initial weights and biases of ANN to improve the accuracy of this artificial intelligence technique. The accuracy of the proposed expert system is validated by comparing obtained results with those from the literature. The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties. The MFA-ANN is also much faster at solving problems. Therefore, the proposed approach can provide an efficient and accurate tool to predict and design HPC. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： High-performance concrete Data mining Evolutionary optimization Artificial neural network modified firefly algorithm

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modified firefly algorithm for Improved Maximum Power Extraction on Wind Energy Conversion System

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2018年第3期8卷 1208-1216页

作者： Putri, Ratna Ika Mahmudi, Irwan Pujiantara, Margo Ardyono, P. Taufik, Taufik Hery, Mauridhi P. State Polytech Malang Dept Elect Engn Malang East Java Indonesia Tadulako Univ Dept Elect Engn Tadulako Indonesia Sepuluh Nopember Inst Technol Fac Elect Technol Dept Elect Engn Surabaya Indonesia Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA USA

This paper presents a maximum power extraction and control system using modified firefly algorithm (MFA) to achieve high efficiency maximum power extraction from a grid-tied wind turbine using permanent magnet synchronous generator (PMSG). To get maximum power extraction from the PMSG, the MFA adjusts duty cycle of boost converter based on rectifier's output voltage and current. Simulation results show that MFA successfully achieves tracking capability under varying wind speed to acquire the maximum power during constant power coefficient operation of the wind turbine. The MFA also yields higher efficiency, faster response and lower integral of time and absolute error (ITAE) compared to the particle swarm optimization (PSO) and the perturb and observe (P&O) counterparts. The proposed MFA was further tested empirically against P&O method to extract maximum power on a 500W lab-scale prototype of wind energy conversion system (WECS). The hardware test results demonstrate that MFA outperforms P&O method when used as an MPPT controller algorithm and yields greater efficiency. The efficiency of PMSG wind turbine with MFA is 93.63 %, while P&O produce 81.54 %.

关键词： modified firefly algorithm maximum power wind turbine PMSG

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An efficient joint spectrum and power allocation in cognitive radio networks using a modified firefly algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS 2017年第2期19卷 214-236页

作者： Tounsi, Abdelkader Babes, Malika Univ Badji Mokhtar Dept Comp Sci Syst & Networks Lab Annaba Algeria Univ Badji Mokhtar Dept Comp Sci Annaba Algeria

In this paper, the applicability and efficiency of the new meta-heuristic algorithm called firefly algorithm to solve the joint power control and channel allocation problem in cognitive radio networks is studied. A modified version of the firefly algorithm (MFA) using the new attractiveness factor is proposed to solve the problem. Theoretical analysis is presented to prove effectiveness and the existence of Nash equilibrium of the proposed strategy. Simulation results of this study show that the proposed power control based on modified firefly algorithm cannot only able to find the optimal transmit power that maximises each secondary's own utility but also improves the fairness among secondary users and satisfies the outage probability constraints. Our results show that the proposed method outperforms previous approaches (including the standard firefly algorithm) in terms of convergence speed.

关键词： cognitive radio network firefly algorithm power control channel allocation outage probability fairness Nash equilibrium game theory modified firefly algorithm attractiveness factor

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Energy Hub Optimization Using modified firefly algorithm 8

Energy Hub Optimization Using Modified Firefly Algorithm

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8th International Renewable Energy Congress (IREC)

作者： Abdelaziz, Almoataz Y. El-Zonkoly, Amany M. Eladl, Alaa M. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo Egypt AAST Coll Engn & Tech Dept Elect & Control Engn Alexandria Egypt

ISBN: (纸本)9781509067510

With the continuous increase of energy demand along with the congested transmission systems, distributed generation (DG) is suggested as a possible solution. In addition, with the limited fossil energy resources, the need for sustainable and environmentally friendly energy sources is increased. Energy hubs as one type of DG, have made the local use of multi energy carrier systems economically attractive. In this paper, a modified firefly based algorithm is proposed to optimally dispatch the energy hub input energy carriers. The objective is to minimize the total energy cost and emission amount of the energy hub while maximizing the cost of electrical and heating energy sold back to the network. The proposed algorithm is applied to a commercial facility (hospital) as a case study. The results show the effectiveness of the proposed algorithm in solving the optimal dispatch of the energy hub components while reducing the overall cost and satisfying the system operational constraints.

关键词： Energy hubs optimal dispatch modified firefly algorithm

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modified firefly algorithm (MFA) Based Vector Quantization for Image Compression 2nd

Modified Firefly Algorithm (MFA) Based Vector Quantization f...

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2nd International Conference on Computational Intelligence in Data Mining (ICCIDM)

作者： Chiranjeevi, Karri Jena, Uma Ranjan Krishna, B. Murali Kumar, Jeevan VSSUT Dept Elect & Telecommun Engn Burla 768018 Odisha India Sri Sivani Inst Technol Dept Elect & Commun Engn Srikakulam Andhra Pradesh India

ISBN: (纸本)9788132227311;9788132227298

firefly algorithm optimization is based on the attractiveness/brightness of the firefly. In firefly algorithm, a lighter (lesser fitness function) firefly move towards the brighter firefly (higher fitness function) with amplitude proportional to Euclidean distance between the lighter and brighter firefly. If no such brighter firefly is found then it moves randomly is search space. This random move causes chance of decrement in brightness of the brighter firefly depending on the direction in which it is move. We proposed a modified firefly algorithm in which movement of brighter fireflies is towards the direction of brightness instead of random move. If this direction of brightness is not in the process then firefly is in same position. We call this novel algorithm as MFA-LBG. Experimental results shows that modified firefly algorithm reconstructed image quality and fitness function value is better than the standard firefly algorithm (FA-LBG) and LBG algorithms. It is observed that that modified firefly algorithm convergence time is less than the standard firefly algorithm.

关键词： Vector quantization LBG firefly algorithm modified firefly algorithm Attractiveness Fitness function

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