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10th International Renewable Energy Congress (IREC)

作者： Welhazi, Yosra Guesmi, Tawfik Hadj Abdallah, Hsan ENIS Natl Engn Sch Sfax Control & Energies Management CEM Lab Sfax Tunisia

ISBN: (纸本)9781728101392

This paper presents the application of the teaching learning based optimization (TLO) for coordinated tuning of the static var compensator (SVC) and power system stabilizers (PSSs) damping controllers. The objective is to improve the damping of low frequency electromechanical oscillations in a multimachine electric power system. In this work, an eigenvalue-based objective function is used for the coordinated tuning of PSSs and SVC controller parameters. To ensure the robustness of the proposed controller, the tuning process takes a large and pre-specified set of operating conditions into account. The effectiveness of the proposed stabilizer is demonstrated through the eigenvalue analysis and nonlinear time-domain simulation. The results of these studies prove that the simultaneous tuning of the TLO based coordinated controller (TLPSS&SVC) has an excellent capability in damping power system low frequency oscillations and enhances greatly the system's dynamic stability. Moreover, it significantly outperforms both the uncoordinated designed stabilizers of the TLO based PSS (TLPSS) and the TLO based SVC (TLSVC).

关键词： static var compensator power system stabilizer multimachine power system coordinated tuning teaching learning based optimization

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Forecasting of rehabilitation treatment in sufferers from lateral displacement of patella using artificial intelligence

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Sport Sciences for Health 2018年第1期14卷 37-45页

作者： Karimzadehfini, Atiye Mahdavinejad, Reza Zolaktaf, Vahid Vahdatpour, Babak Department of Sports Injuries and Corrective Exercises University of Isfahan Isfahan Iran Department of Physical Medicine and Rehabilitation Isfahan University of Medical Sciences Isfahan Iran

Objectives: In this research, the application of artificial intelligence methods for data analysis named hybrid artificial neural network (ANN) with teaching learning based optimization (TLBO) algorithm to predict of the rehabilitation treatment for females with lateral displacement of the patella (LDP) is demonstrated. Methods: The prediction abilities offered using ANN-TLBO model was presented using available data from 48 female patients referred to physical medicine and rehabilitation clinics of Isfahan Ayatollah Kashani medical center and Al Zahra hospital, Iran. In this modeling, clinical characteristics [weight, height, body mass index (BMI), the degree of LDP, affected side and severity of pain] and demographic characteristic (age) were utilized as the input parameters, while the rehabilitation treatment was the output parameter. Results and discussion: The results indicate a high level of efficient of ANN-TLBO model used with an accuracy level of more than 86%. Therefore, this model can be used successfully for the prediction of rehabilitation treatment for females with LDP. © 2017, Springer-Verlag Italia S.r.l.

关键词： Artificial neural network Lateral displacement of patella Rehabilitation treatment teaching learning based optimization

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Transmission Congestion Management in Restructured Power System by Rescheduling of Generators Using TLBO

Transmission Congestion Management in Restructured Power Sys...

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IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE)

作者： Bhattacharya, Sayantan Kuanr, Biswa Ranjan Routray, Aliva Dash, Abhishek Jadavpur Univ Power Engn Dept Kolkata W Bengal India VSSUT Elect Engn Dept Burla India Natl Inst Technol Elect Engn Dept Kozhikode Kerala India

ISBN: (纸本)9781509049967

Congestion management in transmission line is one of the most difficult chores faced by system operator in the rising restructured power system. Congestion in transmission line creates problem for smooth operation in restructured electricity market and increases transmission cost, which leads to profit minimization of the electric power seller and decreases the market efficiency. One of the most effective techniques to get rid of congestion is active power rescheduling of generators. The primary objective of this work is to minimize the rescheduling cost while simultaneously managing the congestion. For optimization of rescheduling cost one of the most recently developed meta-heuristic technique teaching learning based optimization (TLBO) is employed. In order to establish the usefulness of the proposed methodology it is applied on the standard IEEE 30 bus system to manage three different congestion scenarios at optimal cost. Results thus obtained are found to be qualitatively better than some other standard optimization techniques.

关键词： Generator rescheduling Congestion management Generator sensitivity factor Restructured power system teaching learning based optimization

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Neuro Structure optimization Using Adaptive Particle Swarm optimization

Neuro Structure Optimization Using Adaptive Particle Swarm O...

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1st International Conference on Intelligent Computing, Communication and Convergence (ICCC)

作者： Panigrahi, Sushanta Kumar Sahu, Amaresh Pattnaik, Sabyasachi Intersci Inst Management & Technol Bhubaneswar Odisha India Siksha O Anusandhan Univ Bhubaneswar Odisha India Fakir Mohan Univ Balasore Odisha India

In this paper a method to optimize the structure of neural network named as Adaptive Particle Swarm optimization (PSO) has been proposed. In this method nested PSO has been used. Each particle in outer PSO is used for different network construction. The particles update themselves in each iteration by following the global best and personal best performances. The inner PSO is used for training the networks and evaluate the performance of the networks. The effectiveness of this method is tested on many benchmark datasets to find out their optimum structure and the results are compared with other population based methods and finally it is implemented on classification using neural network in data mining. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Artificial neuralnNetwork particle swarm optimization teaching learning based optimization

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Intelligent Overcurrent and Distance Relays Coordination:A Comparative Analysis using GA,PSO and TLBO 4

Intelligent Overcurrent and Distance Relays Coordination:A C...

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4th International Conference on Electrical Energy Systems (ICEES)

作者： Roy, Saptarshi Babu, P. Suresh Babu, N. V. Phanendra NIT Warangal EED Hanamkonda Telangana India

ISBN: (纸本)9781538636954

Relay coordination is important for maintaining proper power system operation and control. Relays should be organized in a special way such that every relay should have a backup and coordination time interval (CTI) between primary and back up and different zones of the relays should be maintained. The relays should operate in minimum desirable time satisfying all the co-ordination constraints optimally. Heuristic techniques are frequently used to get optimal solution of this kind of problems. The problem is solved using teaching learning based optimization(TLBO). The results are compared with the results obtained by Genetic Algorithm(GA) and Particle Swarm optimization(PSO) on a 6bus system. Selection of intelligent over current relay characteristics are incorporated to get the desired results in this work. The results obtained from TLBO are found better than the same obtained from GA and PSO.

关键词： Coordination of relay Coordination time interval teaching learning based optimization Time setting multiplier intelligent relay characteristics Convergence characteristics GA PSO

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Reliability based source maintenance scheduling using nature inspired algorithm

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INTERNATIONAL JOURNAL OF QUALITY & RELIABILITY MANAGEMENT 2015年第1期32卷 81-96页

作者： Abirami, M. Subramanian, S. Ganesan, S. Anandhakumar, R. Annamalai Univ Dept Elect Engn Annamalainagar Tamil Nadu India

Purpose - The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand. Design/methodology/approach - The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as teaching learning based optimization (TLBO) for solving the SMS problem based on reliability. Findings - The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology. Originality/value - The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

关键词： Reliability Maintenance scheduling Reserve loads Resource constraints Safety margin teaching learning based optimization

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Evolving Neuro Structure Using Adaptive PSO and Modified TLBO for Classification 2

Evolving Neuro Structure Using Adaptive PSO and Modified TLB...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Sahu, Amaresh Pattnaik, Sabyasachi Siksha O Anusandhan Univ Bhubaneswar Odisha India Fakir Mohan Univ Bhubaneswar Odisha India

In this paper a method to optimize the structure of neural network named as Adaptive Particle Swarm optimization (PSO) has been proposed. In this method nested PSO has been used. Each particle in outer PSO is used for different network construction. The particles update themselves in each iteration by following the global best and personal best performances. The inner PSO isused for training the networks and evaluate the performance of the networks. The effectiveness of this method is tested on manybenchmark datasets to find out their optimum structure and the results are compared with other population based methods andfinally the optimum structure is implemented usingModified teaching learning based optimization(MTLBO)for classification using neural network in data mining. (C) 2016 The Authors. Published by Elsevier B.V. This is an open acess article under the CC BY-NC-ND license.

关键词： Particle swarm optimization evolutionary computation teaching learning based optimization classification

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TLBO based Droop optimization Technique for DC Microgrid

TLBO based Droop Optimization Technique for DC Microgrid

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IEEE Region 10 Conference on Technology, Knowledge, and Society (TENCON)

作者： Reddy, S. Chakradhar Muthukumari, S. Kumar, T. K. Sunil Kumaravel, S. Kanagalakshmi, S. Natl Inst Technol Elect Engn Dept Calicut Kerala India

ISBN: (纸本)9781728118956

Droop control technique is one of the most commonly used techniques in DC microgrid (MG) to attain proportional load sharing and voltage regulation. Due to the presence of impedance in the transmission line, there are some current sharing errors and voltage deviations across the DC bus. Therefore, loading conditions (LCs) and location of sources in MG have significant effect on current-sharing and voltage regulation. In the present work, droop parameters such as rated voltage and virtual resistance of ith distributed generation (DG) in a MG network are optimized by using optimization procedure by considering all necessary information in order to minimize the impact of line impedances. During the optimization, constraints such as voltage deviations and current sharing errors should be less than five percent of rated voltage and two percent of rated current respectively. By taking combination of voltage regulation and current sharing error, a cost function is formulated. By using load stochastic model, impact of various LCs on the cost functions are weighted. optimization tool called as teaching learning based optimization (TLBO) is then implemented to find out the optimized solution. The proposed algorithm has been illustrated on a low voltage DC MG which comprises of three sources and two loads. Simulation results show effectiveness of proposed method over conventional droop control method in terms of reduced current sharing error and voltage deviations across DC bus.

关键词： Droop control DC microgrid loading conditions teaching learning based optimization

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Evaluation of SFLA and TLBO Algorithm for Path Planning of Mobile Robots in MATLAB 1

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1st International Conference on Energy, Material Sciences and Mechanical Engineering, EMSME 2020

作者： Agarwal, Divya Bharti, Pushpendra S. Guru Gobind Singh Indraprastha University New Delhi110078 India

ISBN: (数字)9789811627941

ISBN: (纸本)9789811627934

Autonomous robots are employed in manufacturing units and industries as they can perform complex tasks in hazardous and harmful environments such as space explorations, submarines, and mining work. When a robot can traverse from start to goal location while evading obstacles, it is called an autonomous robot for which path planning is essential. All evolutionary and swarm intelligence approaches are probabilistic-based and entail few to many control parameters based on problem and application area. Shuffled frog leaping algorithm (SFLA) is one such example that requires six problem-specific parameters, while teaching learning-based optimization (TLBO) is a specific parameter-less algorithm. Thus, there is no burden of tuning control parameters in TLBO. In this work, comparison of both these algorithms is done on MATLAB 2018a to demonstrate the results that despite the usage of no specific parameters, TLBO outperforms SFLA algorithm for path planning and obstacle avoidance problem (PPOA). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Obstacle avoidance Path planning Shuffled frog leaping algorithm teaching learning based optimization

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A comparative study on using metaheuristic algorithms for simultaneously estimating parameters of space fractional advection-dispersion equation

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JOURNAL OF HYDROLOGY 2021年 602卷 126757-126757页

作者： Maroufi, Halimeh Mehdinejadiani, Behrouz Univ Kurdistan Fac Agr Dept Water Sci & Engn Sanandaj Iran

This work developed four inverse models based on Particle Swarm optimization (PSO), Chicken Swarm optimization (CSO), Bees Algorithm (BA), and teaching learning based optimization (TLBO), to identify parameters of space fractional advection-dispersion equation (s-FADE). The s-FADE has four parameters, including average pore-water velocity (nu), fractional dispersion coefficient (D-f), fractional derivative order (alpha), and skewness (beta). A sensitivity analysis indicated that the v is the most effective parameter on the s-FADE results, followed by alpha, D-f, and beta, respectively. The experimental data required were measured at different transport distances of homogeneous and heterogeneous soil columns. Five criteria, namely, convergence trend, objective function value, runtime, repeatability of results, and modeling complexity were used to evaluate algorithm performances and to rank them using a technique for order of preference by similarity to ideal solution (TOPSIS). based on the obtained results, all four algorithms acquired the global optimal values for the nu and alpha parameters using a maximum iteration of 1000 as a stopping criterion and an initial population of 10, while they obtained the relatively different values for the D-f, and beta parameters. The PSO and TLBO algorithms successfully found the global minimum values of the objective functions for both the homogeneous and heterogeneous soils. Among the four algorithms, the TLBO algorithm was the best one in terms of convergence trend, repeatability of results, and modeling complexity, and it was the worst algorithm in term of runtime. Among the PSO, CSO, and BA algorithms, the BA algorithm was superior over the PSO and CSO algorithms in terms of runtime and repeatability of results, while the PSO algorithm was superior over the BA and CSO algorithms in term of converge speed. Overall, according to the results of the TOPSIS method, the TLBO algorithm was the best alternative to estimate the s-FADE

关键词： FracFit toolbox Non-fickian transport Gradient-based optimization algorithms Inverse problem method teaching learning based optimization TOPSIS method

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