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Parameter identification of solid oxide fuel cells with ranking teaching-learning based algorithm

ENERGY CONVERSION AND MANAGEMENT 2018年 174卷 126-137页

作者： Xiong, Guojiang Zhang, Jing Shi, Dongyuan He, Yu Guizhou Univ Coll Elect Engn Guizhou Key Lab Intelligent Technol Power Syst Guiyang 550025 Guizhou Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

The performance of a solid oxide fuel cell (SOFC) is tightly related to relevant parameters associated with the internal multi-physicochemical processes. Accurate identification of these parameters is considerably important for modelling the voltage versus current (V-I) characteristic of SOFCs. In this paper, an improved teaching-learning based algorithm (TLBO) referred to as RTLBO is proposed to identify the exact values for these parameters. The parameter identification of SOFCs is transformed into a minimization optimization problem. The mean square error (MSE) between the measured output voltage and the calculated output voltage is used as the objective function. TLBO has been shown to be competitive with other population-based algorithms. However, its convergence rate is relatively slow especially for complex optimization problems. Inspired by the ranking mechanism in the actual scenarios of teaching-learning process, a ranking based learner selection method is proposed and integrated into both the teacher and learner phases of RTLBO. In RTLBO, poor learners are more likely to be eliminated from the current class in the ranking based teacher phase and good learners are more likely to be chosen to interact with others in the ranking based learner phase, which hence can improve the overall performance of the class quickly. The experimental results on a 5-kW SOFC stack comprehensively demonstrate that RTLBO is able to achieve a better trade-off between the exploration and exploitation compared with twelve advanced TLBO variants and eight popular advanced non-TLBO based methods. In addition, the sensitivity of RTLBO to variations of population size is empirically investigated.

关键词： Solid oxide fuel cell Parameter identification teaching-learning based algorithm Ranking mechanism

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Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2019年第4期34卷 1871-1895页

作者： Tefek, Mehmet Fatih Uguz, Harun Kirsehir Ahi Evran Univ Kaman Vocat Sch Dept Comp Technol TR-40300 Kaman Kirsehir Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42250 Konya Turkey

Economic Dispatch Problem (EDP) is a complex, constrained and non-linear optimization problem. In the EDP, it is aimed to minimize the system fuel cost between minimum and maximum limits of the active power buses. In this study, a modified hybrid Gravitational Search- teaching-learning based Optimization algorithm (MHGT), a quick, efficient and reliable method is proposed by combining standard Gravitational Search algorithm (GSA) and teaching-learning based Optimization (TLBO). The proposed MHGT method was developed by modifying the global search superiority in GSA and powerful local search specialty in TLBO for the solution of constrained optimization problem. The MHGT was tested experimentally by well-known and mostly used ten benchmark function in the literature. The proposed method was first implemented on a 6 bus wind-thermal power system for 400, 450 and 500 MW powers. Then, it was implemented on Turkey 19 bus wind-thermal power system according to different ratios of the installed power as 25, 27.5 and 30 percent to solve the EDP problem. The obtained results were compared with the results of other studies. From the results, it is seen that the proposed MHGT method finds the solution in a short execution time and less fuel cost with more reliably and more efficiently in terms of both fuel cost and execution time.

关键词： Hybrid optimization algorithm gravitational search algorithm teaching-learning based algorithm economic dispatch problem wind-thermal power system

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PID controller for automatic voltage regulator using teaching-learning based optimization technique

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 77卷 418-429页

作者： Chatterjee, Shamik Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

The present work presents teaching-learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Automatic voltage regulator Figure of demerit Sugeno fuzzy logic (SFL) teaching-learning based algorithm

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