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On the significance of the plug setting in optimal time coordination of directional overcurrent relays

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2018年第11期28卷

作者： Gokhale, Sanjeev S. Kale, Vijay S. Visvesvaraya Natl Inst Technol Elect Engn Dept Nagpur Maharashtra India

Directional overcurrent relays give primary plus backup protection to electrical distribution networks. They are coordinated and set at the optimal values to make the total operating time of all the relays in the system minimum. The Time Multiplier Setting (TMS) is used practically to provide time grading to the distribution systems. The Plug Setting (PS) provides for different pick up values for the relays to operate. However, the Inverse Definite Minimum Time relay operating time mathematical expression depends on both these settings. Here, the firefly algorithm is used to demonstrate that, keeping the PS as a variable and the TMS at the lower bound, gives the best results and the role of TMS for time grading is relegated. This is the novelty of this paper. It is stressed that researchers are minimizing operating time at the expense of downsizing the role of TMS as a time grader.

关键词： firefly algorithm overcurrent relay coordination plug setting time multiplier setting

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Optimization of Air Handler Controllers for Comfort Level in Smart Buildings Using Nature Inspired algorithm

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ENERGIES 2023年第24期16卷 8064页

作者： Aziz, Miqdad Kadir, Kushsairy Azman, Haziq Kamarul Vijyakumar, Kanendra Univ Kuala Lumpur British Malaysian Inst UniKL BMI Dept Elect Technol Sect Kuala Lumpur 53100 Malaysia Univ Kuala Lumpur British Malaysian Inst UniKL BMI Dept Elect Engn Sect Kuala Lumpur 53100 Malaysia Univ Teknol MARA UiTM Dept Elect Engn Shah Alam 40450 Malaysia

This research seeks to improve the temperature control of AHU in building sub-levels using optimization algorithms. Specifically, the study applies the FA and PSO algorithms to optimize the PID control of AHU's temperature. The study addresses the issue of temperature control in building sub-levels, which is a common challenge in HVAC systems. The study uses optimization algorithms and a nonlinear model to improve temperature control and reduce fluctuations in temperature from the desired setting. Additionally, a NL-ARX algorithm is utilized to create a nonlinear model based on the thermal dynamics and energy behavioral patterns of ACMV cooling systems. The study evaluates the performance of three controllers-PID, FA-PID, and PSO-PID-based on ITSE as a performance index. The study compares the performance of these controllers to achieve the desired temperature setting, and it analyses the influence of temperature regulation on occupant comfort levels. In this study, we compare different controllers using ITSE as a performance indicator. This shows how well different optimization algorithms work at setting the right temperature. The research gap is the lack of efficient temperature control solutions in building sub-levels that can optimize occupant comfort and energy efficiency. The experimental findings confirm that PSO-PID outperforms conventional PID and FA-PID optimization in terms of achieving the goal objective via computational complexity. Overall, this study's focus is to explore and compare different optimization algorithms to improve temperature control and occupant comfort in building sub-levels.

关键词： air-conditioning and mechanical ventilation air handling unit energy efficiency firefly algorithm particle swarm optimization smart building thermal comfort

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A many-objective optimization charging scheme for wireless rechargeable sensor networks via mobile charging vehicles

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COMPUTER NETWORKS 2022年 215卷 1页

作者： Li, Jiahui Sun, Geng Wang, Aimin Lei, Ming Liang, Shuang Kang, Hui Liu, Yanheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China

In wireless rechargeable sensor networks (WRSNs), the energy can be transferred from the mobile charging vehicles (MCVs) to sensor nodes via the wireless medium, so that providing a new paradigm to prolong the network lifetime. However, the network lifetime of MCV-enabled WRSNs is affected by several factors such as the charging priority of sensor nodes. Moreover, the communication protocols can dynamically affect the energy consumptions of sensor nodes, which should be taken into account. In this paper, we consider a low energy adaptive clustering hierarchy (LEACH)-based WRSNs, and formulate an MCV deployment manyobjective optimization problem (MDMaOP) for replenishing energy to sensor nodes, in which the number of sensor nodes within the charging ranges of the MCVs, motion energy consumptions of the MCVs, remaining energy of the node with the least residual energy and distance between the MCVs and sensor nodes are simultaneously optimized, to prolong the network lifetime. Moreover, the formulated MDMaOP is analyzed and proven as NP-hard. Then, we propose a fast optimization approach and an accurate optimization approach to solve the formulated problem for satisfying the demands of calculation time and accuracy in different scenarios. Simulation results demonstrate that the proposed methods are effective for prolonging the network lifetime and outperform some other comparison algorithms.

关键词： Wireless rechargeable sensor networks Mobile charging vehicles Charging efficiency Many-objective optimization firefly algorithm Non-dominated sorting genetic algorithm II

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Otimização simultânea de posições e forças de amortecedores de vibração por atrito para controle de vibrações de estruturas

Otimização simultânea de posições e forças de amortece...

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作者： Ontiveros Pérez, Sergio Pastor

The damper’s optimization is a new area that has been explored in recent years. There are several classics and newer methods that are competing in reliability, efficiency and speed in achieving a great result. The algorithms are classified as deterministic, using gradient information, or use the function values and their derivatives, and meta- heuristic optimization algorithms are random probabilistic methods that are not based on gradient using only the evaluation of the objective function. The firefly algorithm is a relatively new meta-heuristic algorithm inspired on the behavior of the light of fireflies. This work proposes a method for the friction damper’s optimization using meta-heuristic algorithm. The proposed method is tested in two structures: a nine story building and a sixteen story building. They were submitted to two seismic excitations each. The optimization has one main goal: to reduce the dynamic response in terms of the maximum displacement at the top of the structures obtained by a programmed algorithm based on the central finite difference method, optimizing the location of a maximum number of dampers and their friction’s forces. In the case of the nine story building, the maximum number of dampers is four, and in the case of the sixteen story building the maximum number is six. The results showed that for the two cases studied, the displacement at the top of the structure decreases by more than 50%, concluding that the programmed method is effective and the firefly algorithm is appropriate to get the positions and friction’s forces optimal. Therefore, it is believed that the proposed method can be used as a tool to aid in the design of friction dampers. ...

关键词： Optimization of passive dampers Amortecedores por atrito Otimização matemática Friction damper Vibração Vibration control Estruturas (Engenharia) Seismic excitation firefly algorithm Dissertação

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Developing an algorithm for Buildings Extraction and Determining Changes from Airborne LiDAR, and Comparing with R-CNN Method from Drone Images

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REMOTE SENSING 2019年第11期11卷 1272-1272页

作者： Pirasteh, Saied Rashidi, Pejman Rastiveis, Heidar Huang, Shengzhi Zhu, Qing Liu, Guoxiang Li, Yun Li, Jonathan Seydipour, Erfan Southwest Jiaotong Univ SWJTU Fac Geosci & Environm Engn Dept Surveying & Geoinformat Western Pk Hitech Ind Dev Zone Chengdu 611756 Sichuan Peoples R China Univ Waterloo Mobile Sensing & Data Sci Lab 200 Univ Ave Waterloo ON N2L 3G1 Canada Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran

The world has experienced urban changes rapidly, and this phenomenon encourages authors to contribute to the United Nations sustainable development goals (SDGs) 2030 and geospatial information. This study presents a proposed algorithm of change detection and extracting the borders of buildings. This proposed algorithm provides a set of instructions to describe the method of solving the problem of how extracting the boundary of buildings from the light detection and ranging (LiDAR) input data incorporating with the firefly and ant colony algorithms. The method has used two different epochs to compare buildings and to identify the type of changes in selected buildings. These changes are based on the newly built or demolished buildings. We also used drone images and mask the region-based convolutional neural network (R-CNN) method to compare the results of roof extraction of buildings vs. the proposed algorithm. This study shows that the proposed algorithm identifies the changes of all buildings with higher accuracy of extracting border of buildings than the existing methods, successfully. This study also determines that the amount of root mean square error (RMSE) is 2.40 m(2) when we use LiDAR. This proposed algorithm contributes to identifying rapidly changed buildings, and it is helpful for global geospatial information of urban management that can add best practice and solution toward the UN SDGs connectivity dilemma of urban settlement, resilience, and sustainability.

关键词： LiDAR change detection border extraction firefly algorithm ant colony algorithm drone images R-CNN

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Fork -shaped constructal fin array design a better alternative for heat and mass transfer augmentation under dry, partially wet and fully wet conditions

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2020年 152卷 106329-106329页

作者： Hazarika, Saheera Azmi Bhanja, Dipankar Nath, Sujit Natl Inst Technol Silchar Dept Mech Engn Silchar 788010 Assam India

In actual practical situations, the evaporator fins of refrigeration and air-conditioning apparatuses may operate under dry, fully wet or partially wet conditions. Thus in the present study, the optimum design of an array of constructal fork shaped fins operating under partially wet conditions has been determined by using a linear variation of the humidity ratio of saturated air with the corresponding fin surface temperature. The point of separation between the dry and wet portions may be located either in the stem or branch part of the fin and hence, two cases having different governing equations and boundary conditions are analysed in the current study. The optimisation study is done by maximising the total heat transfer rate of the fin array and un-finned surface and the constraints are imposed in such a way that fin material limitations, minimum fin gap requirements as well as space limitations in the radial direction are taken into account. For obtaining the optimum condition, a swarm based meta heuristic algorithm called firefly algorithm has been employed. Arrays of fork-shaped fin having two and three numbers of branches are analysed in the present study and a comparative study is done with the rectangular fin array. For more compact evaporators, it would be more preferable to select the fin array that results in larger optimum heat transfer rate as well as that having a smaller total length of each fin. An important finding is that the array of fork-shaped fins with two branches give higher optimum heat transfer rate than that from the rectangular fin array in all the cases of different operating conditions. However, no advantage with respect to either higher optimum heat transfer rate or smaller fin length is obtained when the number of branches of fork shaped fins is increased from two to three.

关键词： Constructal Heat and mass transfer Partially wet Constrained optimisation firefly algorithm

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Effective Loss Minimization and Allocation of Unbalanced Distribution Network

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ENERGIES 2017年第12期10卷 1931页

作者： Kaur, Manvir Ghosh, Smarajit Thapar Univ Dept Elect & Instrumentat Engn Patiala 147004 Punjab India

An efficient distribution network must be able to supply power with good voltage profile. The main objective of the proposed work is to allocate losses of the unbalanced distribution network by the firefly algorithm in regulated and deregulated environments before and after loss minimization. Reconfiguration is one of the methods for loss reduction of unbalanced distribution network. Further, optimal placement of distributed generation and capacitor in the reconfigured unbalanced distribution network can further reduce the loss. The results of reconfigured unbalanced distribution network in regulated environment have already been reported. In this paper reconfiguration of an unbalanced distribution network in a deregulated environment is also carried out using an established Fuzzy firefly algorithm. Loss sensitivity factor of unbalanced distribution networks is used to get the appropriate location of distributed generation and capacitor to be placed in the unbalanced distribution network. Their ratings have been found out by using bacteria foraging optimization algorithm (BFOA). The suggested loss allocation method using firefly algorithm is implemented at first on 13 node unbalanced distribution network to check the performance of the proposed loss allocation method when compared to other available method. Finally the proposed method has been implemented on 25 node unbalanced distribution network. Both of the implementations are carried out under MATLAB environment.

关键词： network reconfiguration distributed generation capacitor loss allocation fuzzy logic bacteria foraging optimization algorithm (BFOA) firefly algorithm

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Internet of things based smart city design using fog computing and fuzzy logic

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SUSTAINABLE CITIES AND SOCIETY 2022年第0期79卷 103712-103712页

作者： Bhardwaj, Kartik Krishna Banyal, Siddhant Sharma, Deepak Kumar Al-Numay, Waleed Netaji Subhas Univ Technol Dept Instrumentat & Control New Delhi India Netaji Subhas Inst Technol New Delhi India Netaji Subhas Univ Technol Dept Informat Technol New Delhi India King Saud Univ Comp Sci Dept Riyadh Saudi Arabia

The prevalence of IoT based technology in contemporary society allows for the planning of smart city infrastructure with individually owned devices as participating nodes in a social smart network. Edge computing application into such an IoT network facilitates the distributed computing framework by bringing the computational application closer to the end-users in form of Fog sub-networks, comprising the local participating end user IoT nodes as service requesters and specialized servers catering to the requests, modelled for the infrastructure category they are delivering (Smart healthcare, ITS, Smart architecture, etc.). Opportunistic-based last mile connectivity between the Fog servers and participating nodes aides the dynamicity of the topology for efficient information. Aiding the same, the proposed edge computing enabled, sustainable, opportunistic IoTbased intuitive data exchange, collection and operation scheme that employs a dynamic routing procedure that considers human interactions, their movement patterns and their decision-making process. The proposed scheme employs probabilistic history-based parameters that keep track of node interactions. The interactions are modelled on the firefly PSO, allowing exploitation of the social mobility. The Fuzzy logic is used for hop decision making based on the social and attribute-based parameters which mimic intuitive and pragmatic decision making of humans in terms of social interactions and mobility.

关键词： Internet of things Opportunistic networks Smart city architecture Edge computing Fog computing firefly algorithm Fuzzy logic Nature inspired algorithm Probabilistic routing

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Assessment of Artificial Intelligence-Based Models and Metaheuristic algorithms in Modeling Evaporation

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JOURNAL OF HYDROLOGIC ENGINEERING 2019年第10期24卷

作者： Zounemat-Kermani, Mohammad Kisi, Ozgur Piri, Jamshid Mandavi-Meymand, Amin Shahid Bahonar Univ Kerman Dept Water Engn Kerman 76169 Iran Ilia State Univ Fac Nat Sci & Engn GE-0162 Tbilisi Georgia Univ Zabol Dept Water Engn Soil & Water Coll Zabol 98615 Iran

Evaporation (Ep) has a vital importance for the management and development of water resources projects. In this study two scenarios are considered in prediction of monthly pan evaporation. The first scenario challenges the ability of three artificial intelligence-based models [neural network autoregressive with exogenous input (NNARX), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS)]. The second scenario investigates the capability of five different metaheuristic algorithms [particle swarm optimization (PSO), firefly algorithm (FA), artificial bee colony (ABC), continuous ant colony optimization (CACO), and genetic algorithm (GA)] integrated with the ANFIS model in Ep modeling. Meteorological factors (monthly air temperature, solar radiation, relative humidity, and wind speed data) of two stations in Turkey were used as inputs to the models. Various statistic measures [root-mean-square error (RMSE), mean absolute error (MAE), and determination coefficient (R2)] and diagnostic analysis (Taylor diagram) were deployed to evaluate and compare the performance of the models. The results of the first scenario show that the ANFIS model gave better performance in Gaziantep Station, whereas the NNARX model performed better in estimating Ep values in Adiyaman Station. In the second scenario, it was observed that the PSO and GA algorithms performed better in comparison to the other algorithms in Gaziantep and Adiyaman stations, respectively. The non-parametric Kruskal-Wallis test denoted that there is a significant difference (alpha of 0.05) between the observed versus predicted amounts of monthly Ep for the NNARX, ANFIS, and GEP. However, there is no sign of significant difference in predicting monthly Ep between the applied metaheuristic algorithms.

关键词： Taylor diagram Soft computing firefly algorithm Artificial bee colony Particle swarm optimization Continuous ant colony optimization

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Optimal Wireless Sensor Placement in Structural Health Monitoring Emphasizing Information Effectiveness and Network Performance

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JOURNAL OF AEROSPACE ENGINEERING 2021年第2期34卷 04020112-04020112页

作者： Zhou, Guang-Dong Yi, Ting-Hua Xie, Mei-Xi Li, Hong-Nan Xu, Jun-Hong Hohai Univ Coll Civil & Transportat Engn 1 Xikang Rd Nanjing 210098 Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn 2 Linggong Rd Dalian 116023 Peoples R China Dalian Univ Technol Sch Civil Engn 2 Linggong Rd Dalian 116023 Peoples R China Nanjing Forestry Univ Coll Civil Engn 159 Lonpan Rd Nanjing 210037 Peoples R China

A wireless sensor network (WSN) eliminates physical cables and reduces the difficulty of structural information collection in a structural health monitoring (SHM) system. This paper presents a methodology for placing wireless sensors in SHM systems to obtain the most useful information and the highest WSN performance. The problem of optimal wireless sensor placement (OWSP) first was formulated as a multiobjective optimization in which the information effectiveness is specified as the modal assurance criterion and the network performance is measured by the energy efficiency combined with the network connectivity. Then a multiobjective discrete firefly algorithm based on neighboring searching (MDFA/NS) is proposed to solve the rather difficult combinatorial optimization problem presented by the OWSP. The neighboring information instead of global knowledge is utilized to drive fireflies to move toward the Pareto front at high speed. Elite preservation was adopted to enhance algorithm robustness. Numerical experiments demonstrated that the optimal criterion maintains a trade-off between information effectiveness and network performance, and the MDFA/NS outperforms the popularly used nondominated sorting genetic algorithm II.

关键词： Structural health monitoring Optimal sensor placement firefly algorithm Wireless sensor network Multiobjective optimization

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