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Probabilistic optimal planning in active distribution networks considering non-linear loads based on data clustering method

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第4期16卷 686-702页

作者： Ebrahimi, Hasan Rezaeian-Marjani, Saeed Galvani, Sadjad Talavat, Vahid Urmia Univ Fac Elect & Comp Engn Dept Power Engn Orumiyeh Iran

Renewable energies have a significant portion in supplying energy demands in modern distribution networks. Due to the wide use of power electronic devices, these networks may have power quality problems. The unpredictable nature of renewable energies, besides the effect of non-linear loads brings out serious planning and operating challenges for distribution systems. Basicly, harmonic distortion is a severe problem for both electric efficiency and power energy customers. This study proposes an optimal scheduling strategy for wind turbine's integrated distribution networks with non-linear loads using a multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm and the best solution is selected via the TOPSIS technique. In the proposed strategy, energy storage systems are optimally scheduled besides wind turbines, and reactive power compensators. Also, to use the distribution network more efficiently, an optimal network reconfiguration is applied. The wind turbine's output and load demands have probabilistic nature. The proposed scheme reduces the total harmonic distortion as well as total costs. The efficacy of the proposed management scheme is investigated using the IEEE standard 33 bus distribution network. Also, the performance of the multi-objective individualized instruction mechanism teaching-learning-based optimization algorithm is compared with the multi-objective particle swarm optimization algorithm.

关键词： energy storage systems power system harmonics particle swarm optimisation reactive power multiobjective particle swarm optimization algorithm harmonic distortion renewable energies TOPSIS technique total harmonic distortion multiobjective individualized instruction mechanism distribution systems wind turbines power quality problems energy demands reactive power compensators teaching-learning-based optimization algorithm probabilistic optimal planning optimal network reconfiguration probabilistic nature electric efficiency data clustering method optimal scheduling strategy power energy customers nonlinear loads TOPSIS power supply quality power distribution planning power electronic devices IEEE standard 33 bus distribution network active distribution networks

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Experimental investigation on submerged arc welding of Cr-Mo-V steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第1-4期69卷 93-106页

作者： Rao, R. Venkata Kalyankar, V. D. SV Natl Inst Technol Dept Mech Engn Surat 395007 India

Welding aspects of a high-quality Cr-Mo-V steel are investigated in the present work. Cr-Mo-V steel can be suggested as a best choice for fabrication of pressure vessels to be operated in high-temperature operating conditions. Welding of this group of steel demands very critical attention on the parameters setting of chosen welding process. Only a few researchers had carried out research on the optimization aspects of the submerged arc welding of Cr-Mo-V steel. In the present work, complete experimental analysis is carried out on the submerged arc welding of Cr-Mo-V steel. The important input process parameters considered are welding current, voltage, welding speed, and wire feed. The effect of these input parameters is studied on various responses related to weld bead geometry and few mechanical properties. Taguchi's L-9 orthogonal array is used for design of experiment and the mathematical models are developed for the responses using MINITAB 15 software. The models developed are validated by conducting more experiments. Optimised parameter setting is also obtained by using a recently developed teaching-learning-based optimization algorithm.

关键词： Cr-Mo-V steel Submerged arc welding Process parameters teaching-learning-based optimization algorithm

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Optimizing Tunnel Excavation: Intelligent algorithms for Accurate Overbreak Prediction

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MINING METALLURGY & EXPLORATION 2024年第5期41卷 2525-2538页

作者： Fattahi, Hadi Nejati, Hamid Reza Ghaedi, Hossein Arak Univ Technol Fac Earth Sci Engn Arak Iran Tarbiat Modares Univ Sch Engn Rock Mech Div Tehran Iran

Excavating tunnels has become a widespread practice in the modern world, driven by the need for efficient transportation, subterranean storage, and mineral supply. One challenge encountered during tunnel excavation is the overbreak (OB) phenomenon, particularly prominent when utilizing drilling and blasting techniques. OB poses a risk by increasing operational expenses and compromising workplace safety. Therefore, accurately predicting the occurrence of OB during tunnel excavation is crucial. While various methods exist to forecast OB, traditional approaches like experimental, analytical, numerical, and regression methods face limitations due to uncertainties in geological and geotechnical parameters. In this paper, the use of teaching-learning-based optimization (TLBO) and Firefly (FF) algorithms is proposed to predict OB, aiming to fully comprehend the physical and mechanical characteristics of the rock mass while considering uncertainties and optimizing project completion in terms of cost and time. The model was constructed using data from three case studies: an Indian mine;the Azad tunnel on the Tehran-North route in Alborz, Iran;and the underground coal mine Tarzareh, comprising 217 data points. Parameters affecting the OB phenomenon in this study include rock mass rating (RMR), specific drilling (SD), perimeter holes powder factor (PPF), and spacing to burden ratio of contour holes (S/B). The dataset was divided into two groups: 80% for training the model and 20% for testing the relationship. To evaluate the model, statistical indices such as squared correlation coefficient (R2), root mean square error (RMSE), and mean square error (MSE) were used. The validation results indicated that the TLBO and FF algorithms performed satisfactorily, demonstrating high accuracy and low error. This suggests that engineers, scientists, and practitioners can benefit from employing intelligent approaches in mining and rock mechanics-related operations, utilizing the accurate m

关键词： Tunnel excavation Overbreak phenomenon teaching-learning-based optimization algorithm Firefly algorithm

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An improved explicit double-diode model of solar cells: Fitness verification and parameter extraction

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ENERGY CONVERSION AND MANAGEMENT 2018年 169卷 345-358页

作者： Chen, Yujie Sun, Yize Meng, Zhuo Donghua Univ Coll Mech Engn 2999 North Renmin Rd Shanghai 201620 Peoples R China

Accurate simulation of photovoltaic characteristics is now a mandatory obligation before validating an experiment;hence, accurate model and parameters of solar cells are indispensable. This paper presents an improved explicit double-diode model based on the Lambert W function (EDDM-LW), and then compares the fitness and parameter extraction performance. By defining two new parameters (K and r) to separate the exponential function in double-diode model (DDM) and using the Lambert W function, the explicit expression for I-V characteristics is proposed. In contrast to exiting works, the new parameters can readily be computed by the electrical characteristics of the standard test condition without an implicit characteristic. To verify the accuracy of the proposed model, the fitness difference is first investigated with a solar cell and three different types of solar modules. The results indicate that under the same parameter values, EDDM-LW achieves the lowest root mean square error value and exhibits better fitness in representing the I-V characteristics. In addition, the optimal parameters are extracted by an improved teaching-learning-based optimization algorithm. The experimental results show that the optimal parameter values extracted from EDDM-LW are more accurate than those extracted from DDM. based on these observations, EDDM-LW can be deemed a useful and practical model for the simulation, evaluation, and optimization of the photovoltaic system.

关键词： Solar cells Double-diode model Explicit model Parameter extraction teaching-learning-based optimization algorithm

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Set Theoretical Variants of optimization algorithms for System Reliability-based Design of Truss Structures

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第3期65卷 717-729页

作者： Kaveh, Ali Hamedani, Kiarash Biabani Kamalinejad, Mohammad Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

In this paper, recently developed set theoretical variants of the teaching-learning-based optimization (TLBO) algorithm and the shuffled shepherd optimization algorithm (SSOA) are employed for system reliability-based design optimization (SRBDO) of truss structures. The set theoretical variants are designed based on a simple framework in which the population of candidate solutions is divided into some number of smaller well-arranged sub-populations. In addition, the framework is applied to the Jaya algorithm, leading to a set-theoretical variant of the Jaya algorithm. So far, most of the reliability-based design optimization studies have focused on the reliability of single structural members. This is due to the fact that the optimization problems with system reliability-based constraints are computationally expensive to solve. This is especially the case of statically redundant structures, where the number of failure modes is so high that it is impractical to identify all of them. System-level reliability analysis of truss structures is carried out by the branch and bound method by which the stochastically dominant failure paths are identified within a reasonable time. At last, three numerical examples, including size optimization of truss structures, are presented to illustrate the effectiveness of the proposed SRBDO approach. The results indicate the efficiency and applicability of the set theoretical optimization algorithms to solve the SRBDO problems of truss structures.

关键词： teaching-learning-based optimization algorithm shuffled shepherd optimization algorithm Jaya algorithm truss structure structural optimization structural reliability theory

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Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms

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ENGINEERING optimization 2012年第8期44卷 965-983页

作者： Rao, R. Venkata Patel, Vivek SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This study explores the use of teaching-learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms for determining the optimum operating conditions of combined Brayton and inverse Brayton cycles. Maximization of thermal efficiency and specific work of the system are considered as the objective functions and are treated simultaneously for multi-objective optimization. Upper cycle pressure ratio and bottom cycle expansion pressure of the system are considered as design variables for the multi-objective optimization. An application example is presented to demonstrate the effectiveness and accuracy of the proposed algorithms. The results of optimization using the proposed algorithms are validated by comparing with those obtained by using the genetic algorithm (GA) and particle swarm optimization (PSO) on the same example. Improvement in the results is obtained by the proposed algorithms. The results of effect of variation of the algorithm parameters on the convergence and fitness values of the objective functions are reported.

关键词： Brayton cycle inverse Brayton cycle teaching-learning-based optimization algorithm artificial bee colony algorithm particle swarm optimization genetic algorithm

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Multi-Response optimization of Al2O3 Nanopowder-Mixed Wire Electrical Discharge Machining Process Parameters of Nitinol Shape Memory Alloy

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MATERIALS 2022年第6期15卷 2018页

作者： Chaudhari, Rakesh Prajapati, Parth Khanna, Sakshum Vora, Jay Patel, Vivek K. Pimenov, Danil Yurievich Giasin, Khaled Pandit Deendayal Energy Univ Sch Technol Dept Mech Engn Raisan 382007 Gandhinagar India Pandit Deendayal Energy Univ Sch Technol Dept Solar Engn Raisan 382007 Gandhinagar India South Ural State Univ Dept Automated Mech Engn Lenin Prosp 76 Chelyabinsk 454080 Russia Univ Portsmouth Sch Mech & Design Engn Portsmouth PO1 3DJ Hants England

Shape memory alloy (SMA), particularly those having a nickel-titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect, better wear resistance, and adequate superelasticity, as well as biocompatibility, make them a preferable alloy to be used in automotive, aerospace, actuators, robotics, medical, and many other engineering fields. Precise machining of such materials requires inputs of intellectual machining approaches, such as wire electrical discharge machining (WEDM). Machining capabilities of the process can further be enhanced by the addition of Al2O3 nanopowder in the dielectric fluid. Selected input machining process parameters include the following: pulse-on time (T-on), pulse-off time (T-off), and Al2O3 nanopowder concentration. Surface roughness (SR), material removal rate (MRR), and recast layer thickness (RLT) were identified as the response variables. In this study, Taguchi's three levels L-9 approach was used to conduct experimental trials. The analysis of variance (ANOVA) technique was implemented to reaffirm the significance and adequacy of the regression model. Al2O3 nanopowder was found to have the highest contributing effect of 76.13% contribution, T-on was found to be the highest contributing factor for SR and RLT having 91.88% and 88.3% contribution, respectively. Single-objective optimization analysis generated the lowest MRR value of 0.3228 g/min (at T-on of 90 mu s, T-off of 5 mu s, and powder concentration of 2 g/L), the lowest SR value of 3.13 mu m, and the lowest RLT value of 10.24 (both responses at T-on of 30 mu s, T-off of 25 mu s, and powder concentration of 2 g/L). A specific multi-objective teaching-learning-based optimization (TLBO) algorithm was implemented to generate optimal points which highlight the non-dominant feasible solutions. The least error between predicted and actual values suggests the effective

关键词： shape memory alloy Al2O3 nanopowder Nitinol surface morphology wire electrical discharge machining teaching-learning-based optimization algorithm

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Structure optimization of Stand-Alone Renewable Power Systems based on Multi Object Function

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ENERGIES 2016年第8期9卷

作者： Cho, Jae-Hoon Chun, Myung-Geun Hong, Won-Pyo Hankyong Natl Univ Smart Logist Technol Inst 327 Chungang Ro Anseong 17579 Kyonggi Do South Korea Chungbuk Natl Univ Dept Elect Engn Cheongju 28644 Chungbuk South Korea Hanbat Natl Univ Dept Bldg Serv Engn 125 Dongseodae Ro Daejeon 34158 South Korea

This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC), loss of power supply probability (LPSP), and the fuel cost of the diesel generator required by the user. A new optimization algorithm and an object function (including a penalty method) are also proposed;these assist with designing the best structure for a hybrid system satisfying the constraints. In hybrid energy system sources such as photovoltaic (PV), wind, diesel, and energy storage devices are connected as an electrical load supply. Because the power produced by PV and wind turbine sources is dependent on the variation of the resources (sun and wind) and the load demand fluctuates, such a hybrid system must be able to satisfy the load requirements at any time and store the excess energy for use in deficit conditions. Therefore, reliability and cost are the two main criteria when designing a stand-alone hybrid system. Moreover, the operation of a diesel generator is important to achieve greater reliability. In this paper, TAC, LPSP, and the fuel cost of the diesel generator are considered as the objective variables and a hybrid teaching-learning-based optimization algorithm is proposed and used to choose the best structure of a stand-alone hybrid PV/wind/diesel/battery system. Simulation results from MATLAB support the effectiveness of the proposed method and confirm that it is more efficient than conventional methods.

关键词： stand-alone hybrid PV/wind/diesel/battery system teaching-learning-based optimization algorithm total annual cost loss of power supply probability

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Crow search algorithm based optimal dynamic performance control of SVC assisted SMIB system 20

Crow search algorithm based optimal dynamic performance cont...

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20th National Power Systems Conference (NPSC)

作者： Kumar, Sudeep Kumar, Akshay Shankar, Gauri Indian Inst Technol ISM Elect Engn Dept Dhanbad Jharkhand India

ISBN: (纸本)9781538661598

A study on improvement of dynamic as well as steady state performance of power system model using static VAR compensator (SVC) is carried out in this paper. SVC refers to a shunt device within the family of flexible alternating current transmission system and is widely used for enhancing voltage profile and power transmission capability. SVC along with power system stabilizer (PSS) is employed to damp out the oscillation and take back the system to a stable state following disturbance. Crow search algorithm (CSA) is implemented to optimize conventional parameters of PSS and SVC. Simulation results found by it are compared with others optimization algorithms such as particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO). Also, power system model under the effect of CSA based tuned PSS and SVC is verified using eigenvalue analysis under steady state condition for investigating steady state stability. A comparative time domain simulation under MATLAB/SIMULINK platform employing CSA, PSO and TLBO tuned PSS and SVC with studied single machine infinite bus system is carried for dynamic stability assessment. Results obtained confirm the efficacy of the CSA in better tuning of PSS and SVC than other counterparts.

关键词： Crow search algorithm particle swarm optimization algorithm power system stabilizer static VAR compensator teaching-learning-based optimization algorithm

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A Novel Frequency and Voltage Control Method for Islanded Micro-grid 9

A Novel Frequency and Voltage Control Method for Islanded Mi...

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9th Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Akbari, Ehsan Mazandaran Univ Sci & Technol Fac Elect & Comp Engn Babol Iran

ISBN: (纸本)9781665405652

Two performance modes, connected to grid and grid independent, can be considered for microgrids. In connected mode, the grid voltage and frequency are controlled by the grid and the microgrid controls the exchange of electrical power with the grid. In a standalone mode where there is no connection between the microgrid and the main grid, the microgrid is responsible for regulating voltage and frequency. One of the critical issues of microgrids, in a separate state from the power system, is frequency and voltage control. In this paper, the improvement of microgrid control performance in the field of voltage and frequency is investigated. voltage and frequency control are investigated to improve the performance of the micro-grid. Different types of distributed generation (DG) have been used to investigate and improve voltage stability. A new combination of teaching-learning based optimization (TLBO) and harmonic search algorithm (HSA) has been used for optimal performance in islanded Microgrid. Performance optimization is accomplished by finding the optimal parameters for the droop coefficient of the distributed generation resources and the reliability of the wind units in order to reduce the cost of generated energy. optimization is defined as a multi-objective problem, and the objective functions are used to minimize fuel consumption of distributed generation and improve the voltage characteristic and microgrid stability of operating and protection constraints. System steady state frequency, reference frequency, droop coefficients and reference voltage of the distributed generation unit are considered as optimization variables. The proposed method is compared with other methods for the 33-bus microgrid using MATLAB software. The simulation results show that the proposed method improves the performance of the microgrid.

关键词： teaching-learning-based optimization algorithm Harmonic search algorithm Microgrid Islanded

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