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Terminal voltage prediction of Li-Ion batteries using Combined Neural Network and teaching learning based optimization algorithm

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APPLIED SOFT COMPUTING 2023年 133卷

作者： Narayanan, S. Siva Suriya Thangavel, S. Natl Inst Technol Puducherry Dept Elect & Elect Engn Karaikal India

The static and dynamic model parameters are critical parameters for the accurate estimation of opencircuit voltage and the terminal voltage of a Lithium-Ion (Li-Ion) battery. This work mainly focuses on the investigation of an accurate method for predicting the open-circuit voltage and terminal voltage of Li-Ion batteries. This work proposes an Enhanced Neural Network and teaching learning-based optimization (ENN-TLBO) algorithm to predict the terminal voltage of a Li-Ion battery used in electric vehicle applications. In this work, the static model is obtained by using a neural network (NN) while the dynamic model parameters are predicted by using the teaching learning based optimization technique (TLBO). In the NN method, different network parameters are varied to predict the accurate State of Charge (SoC)-Open Circuit Voltage (OCV) relation of the battery. The proposed static models are validated by the performance metrics such as RMSE and R2, the best static model is identified by low Root Mean Square Error (RMSE) and high R2 value of the model. In recent times many optimization algorithms are reported in the literature to predict the dynamic model, which requires certain algorithm-specific tuning parameters that affects the performance of the algorithm. The proposed TLBO algorithm is implemented with lesser tuning parameters to predict the hysteresis constant of the dynamic model. The RMSE value of the predicted terminal voltage at different temperature profiles fewer tuning parameters, simple to implement, and high accuracy.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Neural network activation function Machine learning (ML) Open Circuit Voltage (OCV) Battery terminal voltage teaching learning based optimization (TLBO) Training algorithm

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Empirical Study on Clustering based on Modified teaching learning based optimization

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Procedia Computer Science 2016年 92卷 442-449页

作者： Sushanta Kumar Panigrahi Sabyasachi Pattnaik Interscience Institute of Management & Technology Bhubaneswar Odisha India Fakir Mohan University Bhubaneswar Odisha India

In this Paper the focus is given on data clustering using Modified teaching–learning based optimization (MTLBO) a hybridization technique of TLBO. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution and it has been used for clustering. The motivation behind the data clustering is to find inherent structure in the data items and grouping then on the basis of their mutual similarity. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on clustering using neural network in data mining.

关键词： Artificial neural network evolutionary algorithm particle Swarm optimization teaching learning based optimization clustering

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Modified teaching learning based optimization for selective harmonic elimination in multilevel inverters

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AIN SHAMS ENGINEERING JOURNAL 2022年第5期13卷

作者： Yaqoob, Muhammad Tayyab Rahmat, Mohd. Khairil Maharum, Siti Marwangi Mohamad Univ Kuala Lumpur Renewable Energy Res Lab RENERAL Elect Engn Sect British Malaysian Inst Gombak 53100 Selangor Malaysia Univ Kuala Lumpur Elect Technol Sect British Malaysian Inst Gombak 53100 Selangor Malaysia

Renewable Energy sources are becoming an important source of energy generation. Battery energy storages systems are used to supply the power when there is variation in power generation due to climate change. Power stored in batteries need to be converted from Direct current (DC) into Alternating current (AC) current to integrate it with the grids. This paper presents the modification in a recently emerged teaching learning-based optimization (TLBO) to be implemented in multilevel inverters. The results obtained from proposed modified TLBO has been compared with existing TLBO, Particle Swarm optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Imperialistic Competitive Algorithm (ICA). Furthermore, the proposed algorithm has been tested for four well-known mathematical functions like Greiwangks, Ackley's Dejon's and Drop wave and find out that the proposed algorithm has a better convergence rate than normal TLBO. Finally, the optimized switching angles obtained from the proposed algorithm has been tested in SIMULINK by designing an 11-level cascaded H-bridge multilevel inverter and observe the magnitude of individual harmonics. The results obtained from the proposed algorithm shows the better harmonic reduction while comparing with other algorithms. (c) 2022 THE AUTHORS. Published by Elsevier BV

关键词： Algorithm Power electronics teaching learning based optimization Selective harmonic elimination Multilevel inverters

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Design of Controller for Automatic Voltage Regulator Using teaching learning based optimization

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Procedia Technology 2015年 21卷 295-302页

作者： V. Rajinikanth Suresh Chandra Satapathy Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering Chennai 600119 India Department of Computer Science and Engineering ANITS Visakhapatnam 531162 India.

In this paper, One Degree Of Freedom (1DOF) and Two Degrees Of Freedom (2DOF) Proportional + Integral + Derivative (PID) controller design is proposed and implemented on the Automatic Voltage Regulator (AVR) system using traditional teaching learning based optimization (TLBO) algorithm. Minimization of a multi-objective function guides the TLBO algorithm's exploration until the process converges with an optimal solution. A simulation study is carried to examine the performance of TLBO assisted controller design procedure for three, four and five dimensional searches. The performance of the proposed method is validated with most successful heuristic procedures, such as Particle Swarm optimization (PSO), Bacterial Foraging optimization (BFO) and Firefly Algorithm (FA). The result show that, 1DOF PID controller and PID controller with filter offers smooth reference tracking response and the 2DOF PID controller with the Feed Forward (FF) and Feed Back (FB) structure presents reduced time domain and error values compared to the alternatives.

关键词： AVR PID controller degrees of freedom teaching learning based optimization multi-objective function

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A New Variant of teaching learning based optimization Algorithm for Global optimization Problems

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INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS 2019年第1期43卷 65-75页

作者： Kumar, Yugal Dahiya, Neeraj Malik, Sanjay Khatri, Savita JUIT Dept Comp Sci & Engn Solan Himachal Pardes India SRM Univ Dept Comp Sci & Engn Delhi NCR Campus Sonipat Haryana India BMIET Dept Comp Sci & Engn Sonepat India

This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.

关键词： teaching learning based optimization crossover meta-heuristics mutation

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Intelligent Neighbourhood teaching learning based optimization Algorithm

Intelligent Neighbourhood Teaching Learning Based Optimizati...

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Singh, Geetanjali Sharma, Nirmala Sharma, Harish Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509064717

teaching learning based optimization Algorithm (TLBOA) is an efficient approach of dealing with linear, nonlinear and multidimensional optimization problems. It depicts the classroom behaviour of teachers and students (learners). The algorithm is divided in two phases, namely Teacher phase and Learner phase. The position update process of learner phase is based on the difference vector of two randomly selected solutions, which improves the diversification ability of the algorithm. Further, in case of high step size, the solutions may skip the global optima. Therefore, to improve the exploitation ability and to control the step size, a new phase called as intelligent phase inspired from the Onlooker bee phase of Artificial Bee Colony optimization algorithm, is incorporated with the TLBO. In the propounded phase, intelligent solutions will be given more chances in participation of the position update process. This will leads to high convergence in the swarm. The performance of this propounded algorithm is calculated over 15 benchmark functions and compared with other state of art optimization algorithms namely TLBO, Gravitational Search Algorithm (GSA), and Grey Wolf Optimizer (GWO).

关键词： teaching learning based optimization Intelligent Phase Swarm Intelligence based algorithm optimization

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Optimal PD-PID cascaded controller based Automatic Generation Control of a multisource interconnected power system tuned by teaching learning based optimization 7

Optimal PD-PID cascaded controller based Automatic Generatio...

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7th IEEE India International Conference on Power Electronics (IICPE)

作者： Debnath, Manoj Kumar Patel, Nimai Charan Mallick, Ranjan Kumar Siksha O Anusandhan Univ Dept Elect Engn Bhubaneswar Odisha India Siksha O Anusandhan Univ Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781509045303

This research article explains about the implementation of a novel PD-PID cascaded controller for automatic generation control of a two area multi source interconnected power system where in each area three types of generating sources are incorporated namely gas, hydro and thermal unit. teaching learning based optimization technique is used for optimization of the scaling factors of the proposed PD-PID controllers when a sudden load disturbance of 0.01 p.u. is considered in area 1 and Integral time absolute error (ITAE) is chosen as an objective function. The superior performance of the proposed optimal controller is proved by comparing the dynamic response of the system with a recently published result of PID controller optimized by Differential Evolution (DE) technique [16]. The performance of the system is analyzed under two cases: (1) With AC tie-line only and (2) With AC-DC tie-line. The evaluation is carried on considering various time response specifications like settling time, peak undershoots and peak overshoots.

关键词： Automatic Generation Control Proportional Integral-Derivative controller teaching learning based optimization PD-PID cascaded controller Multi source power system

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teaching learning algorithm based optimization of kerf deviations in pulsed Nd:YAG laser cutting of Kevlar-29 composite laminates

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INFRARED PHYSICS & TECHNOLOGY 2018年 89卷 203-217页

作者： Gautam, Girish Dutt Pandey, Arun Kumar Jaypee Univ Engn & Technol Mech Engn Dept Guna MP India Bundelkhand Inst Engn & Technol Mech Engn Dept Jhansi UP India

Kevlar is the most popular aramid fiber and most commonly used in different technologically advanced industries for various applications. But the precise cutting of Kevlar composite laminates is a difficult task. The conventional cutting methods face various defects such as delamination, burr formation, fiber pullout with poor surface quality and their mechanical performance is greatly affected by these defects. The laser beam machining may be an alternative of the conventional cutting processes due to its non contact nature, requirement of low specific energy with higher production rate. But this process also faces some problems that may be minimized by operating the machine at optimum parameters levels. This research paper examines the effective utilization of the Nd:YAG laser cutting system on difficult-to-cut Kevlar-29 composite laminates. The objective of the proposed work is to find the optimum process parameters settings for getting the minimum kerf deviations at both sides. The experiments have been conducted on Kevlar-29 composite laminates having thickness 1.25 mm by using Box-Benkhen design with two center points. The experimental data have been used for the optimization by using the proposed methodology. For the optimization, teaching learning Algorithm based approach has been employed to obtain the minimum kerf deviation at bottom and top sides. A self coded Matlab program has been developed by using the proposed methodology and this program has been used for the optimization. Finally, the confirmation tests have been performed to compare the experimental and optimum results obtained by the proposed methodology. The comparison results show that the machining performance in the laser beam cutting process has been remarkably improved through proposed approach. Finally, the influence of different laser cutting parameters such as lamp current, pulse frequency, pulse width, compressed air pressure and cutting speed on top kerf deviation and bottom kerf devia

关键词： Kevlar composite Nd:YAG laser cutting Kerf deviation Multiple regression analysis teaching learning based optimization

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A hybrid model using teaching-learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第9期12卷 8793-8808页

作者： Thawkar, Shankar Hindustan Coll Sci & Technol Dept Informat Technol Mathura Uttar Pradesh India

Feature selection is the most important step in the design of a breast cancer diagnosis system. The basic objective of the proposed methodology is to reduce the size of the feature space to improve the performance of the classification system. In this article, a hybrid teaching-learning based optimization (TLBO) with a Salp swarm algorithm (SSA) is presented to select the features with an artificial neural network as a fitness evaluator. The features selected by TLBO-SSA are evaluated using an adaptive neuro-fuzzy inference system. The performance of the proposed methodology is tested over 651 mammograms. The experimental results show that TLBO-SSA appears to be the best when compared with the basic TLBO algorithm. TLBO-SSA archived an accuracy of 98.46% with 98.81% sensitivity, 98.08% specificity, 0.9852 F-score, 0.9692 Cohen's kappa coefficient, and area under curve A(Z) = 0.997 +/- 0.001. Again the robustness of the proposed TLBO-SSA method is tested using a benchmark dataset obtained from the UCI repository. The result obtained by TLBO-SSA is compared with the Genetic Algorithm. The results show that TLBO-SSA is better than the Genetic Algorithm.

关键词： teaching learning based optimization Salp swarm algorithm Adaptive neuro-fuzzy inference system Artificial neural network Feature selection Mammography Classification

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Design Technique of Open-Slot Surface-Mounted PM Machines Using Coenergy-based Subdomain Model and teaching-learning-based optimization Algorithm

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IEEE ACCESS 2024年 12卷 88492-88505页

作者： Lee, Do Hyeon Min, Seun Guy Soongsil Univ Dept Elect Engn Seoul 06978 South Korea

The open-slot stator topology, which allows for preformed coils and an excellent fill factor, has been regarded as a suitable candidate for low-speed operation such as direct-drive systems. This paper presents a rapid and accurate design optimization of surface-mounted permanent magnet (PM) machines equipped with the open-slot stator configuration. The analytical method, utilizing the coenergy-based subdomain model, is developed to predict the various electromagnetic performances. The proposed analytical model is decoupled into two components, relevant and irrelevant to the optimization process. Subsequently, the teaching-learning-based optimization (TLBO), a human-based algorithm for global solutions, is applied to the analytical model to search for the best compromise belonging to the Pareto-optimal front. The design methodology presented in this article is well-suited for the open-slot stator configuration, providing a computationally efficient and accurate approach to address multi-constrained minimization problems. In the end, the reliability of the proposed model is verified by finite element (FE) results.

关键词： optimization methods Analytical models Torque Power capacitors Electromagnetics Computational modeling Stator windings learning systems Permanent magnet machines coenergy principle direct drive open-slot stator optimization subdomain model surface-mounted permanent magnet teaching learning based optimization

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