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2nd International Conference on Knowledge based Engineering and Innovation (KBEI)

作者： Roshandel, Emad Gheasaryan, Sayed Mokhtar Saghaian-Nejad, Sayed Morteza Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

ISBN: (纸本)9781467365062

In this paper indirect rotor flux oriented control (LRFOC) approach has been implemented to torque control of induction machine (IM). The drive's control system has two proportional integral (PI) controllers, which have an essential role to control the transient and steady state conditions. So to make a desirable control, teaching learning based optimization (TLBO) method has been used to find the optimum coefficients. In order to enhance operation of the controller and achieve more accurate results the fuzzy logic control method is combined with TLBO. In fact on-line control will be achieved by applying fuzzy system to control system. In other word knowledge of expert human is used to locate fuzzy logic system in optimum location of control system. Also TLBO method by limiting boundaries of membership functions around optimal point lead system toward less complexity and practicability. Simulation results show the effectiveness of the proposed control approach in damping transient oscillations and reduction of steady state error.

关键词： Induction machine teaching-learning based optimization fuzzy logic Field oriented control Vector control Artificial intelligent

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Metaheuristic for Solving Multi-Objective Job Shop Scheduling Problem in a Robotic Cell

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IEEE ACCESS 2020年 8卷 147015-147028页

作者： Li, Xiaohui Yang, Xi Zhao, Yi Teng, Ying Dong, Yuan Changan Univ Sch Elect & Control Engn Xian 710054 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 610051 Peoples R China

This paper deals with the multi-objective job shop scheduling problem in a robotic cell (MOJRCSP). All the jobs are processed according to their operations order on workstations. Different from classical job shop scheduling problem, the studied problem considers that jobs' transportation is handled by a robot. Also, the jobs are expected to be finished in a time window, instead of a constant due date. A mixed Integer Programming (MIP) model is proposed to formulate this problem. Due to the special characteristics of the studied problem and its NP-hard computational complexity, a metaheuristic based on teaching learning based optimization (TLBO) algorithm has been proposed. The proposed algorithm determines simultaneously the operations' assignments on workstations, the robot assignments for transportation operations, and the robot moving sequence. The objective is to minimize the makespan and the total earliness and tardiness. Computational results further validated the effectiveness and robustness of our proposed algorithm.

关键词： Job shop scheduling Approximation algorithms Service robots Workstations Optimal scheduling Robotic cell job shop multi-objective optimization local search teaching-learning based optimization

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Fractional-ordernonlinear PIDcontroller based maximum power extraction method for a direct-driven wind energy system

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第12期30卷

作者： Pathak, Diwaker Bhati, Sachin Gaur, Prerna Univ Delhi Netaji Subhas Inst Technol Instrumentat & Control Engn Dept New Delhi 110078 India

Objective To maintain the operation of a wind turbine at its maximum efficiency and for the extraction of maximum power from the wind. Method In this paper, at first, a novel application of fractional-order nonlinear proportional-integral-derivative (FONPID) controller is proposed in the machine side converter (MSC) control loop of a 2 MW grid-connected wind energy system. Then, the grid side converter (GSC) is controlled to ensure the unity power factor of the understudy system. To extract the maximum power from the wind, the application of the proposed controller is applied to a well-established TSR MPPT control method, which compares optimal and actual values of rotor speed, and generated error is given to the proposed controller to vary the rotor speed accordingly. Moreover, improved MPPT performance offered by the proposed FONPID controller has also been compared to well-established existing controllers based TSR MPPT methods. Furthermore, parameters of the proposed and existing controllers in the TSR MPPT control loop are also tuned using teaching-learning based optimization (TLBO) and obtained performance is compared with the performance of particle swarm optimization (PSO) to show effectiveness. Results The generator speed offered by the proposed FONPID based TSR MPPT method effectively tracks its reference values far better than the extant controller based TSR MPPT method, whereas extant NPID has less overshoot as compared to the extant PID based TSR MPPT method. Conclusion To perform the effective operation of achieving MPP, q-axis current of the stator has been controlled by the proposed FONPID based TSR MPPT method in such a style that rotor speed can be operated at its optimal value where the generator has the maximum power for given wind speed. DC-link voltage is controlled at its rated value to guarantee the unity power factor operation using the GSC controller. FONPID based TSR MPPT method provides a better and robust MPPT operation than the PID, NPI

关键词： fractional-order nonlinear PID grid-tied wind energy system MPPT nonlinear control PMSG teaching-learning based optimization

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Optimal operation of multi-source electric vehicle connected microgrid using metaheuristic algorithm

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JOURNAL OF ENERGY STORAGE 2022年 52卷

作者： Jayalakshmi, N. S. Jadoun, Vinay Kumar Gaonkar, D. N. Shrivastava, Ashish Kanwar, Neeraj Nandini, K. K. Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India NITK Dept Elect & Elect Engn Mangalore 575025 India Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Gurugram 122003 Haryana India Manipal Univ Jaipur Dept Elect Engn Jaipur 303007 Rajasthan India

In this paper, a multi-source microgrid (MG) has been considered which inducts power from solar photovoltaic (PV), wind turbine, pumped hydro storage system (PHSS) and diesel generator (DG). A problem formulation is proposed on a multi-source MG considering an electric vehicle (EV) as source and load demand. A modified operation strategy is proposed to achieve the lowest possible fuel usage of DG and to optimize the operation of multi-sources used in the MG. When the sum of PV, wind power production and EV discharge is less than the load requirement, the required deficit power should be delivered by DG and PHS. This work considers PV and wind as the primary energy supplying sources, while DG, EV and PHS as the additional energy suppliers with EV and PHS as energy storage systems. By properly coordinating EVs, they can become a major contributor to the successful execution of the MG concept. In this work, a modified charging/discharging algorithm is presented to check the effect of EVs to supply a portion of peak loads with PHS to reduce the fuel consumption of DG in three diverse modes of operation. A modified whale optimization algorithm (WOA) and teaching learning-based optimization (TLBO) are applied to effectively solve this proposed complex problem using the MATLAB platform. The optimum solutions obtained after different independent trials by both the techniques are compared with the latest published techniques. It can be observed that modified WOA performs better than TLBO and other recently published methods on the base case and proposed multi-source MG case in three diverse modes of operation. The outcomes of the simulation confirm the effectiveness of modified WOA in reducing fuel consumption.

关键词： Multi-source microgrid Electric vehicle Whale optimization algorithm teaching-learning based optimization Pumped hydro storage Wind power Solar power Diesel generators

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optimization of distribution network operating parameters in grid tied microgrid with electric vehicle charging station placement and sizing in the presence of uncertainties

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2023年

作者： Nandini, K. K. Jayalakshmi, N. S. Jadoun, Vinay Kumar Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal India

The power generated by renewable sources is prone to uncertainties which encourages to perform effective modelling to ensure the reliable operation of the grid-connected microgrid (MG) system. This work focuses on the uncertainty modelling of primary sources of solar power, wind power and electric vehicle (EV) load considering three factors battery capacity, state of charge (SoC) and type of EVs charged by employing Monte-Carlo Simulation (MCS) to minimize the distribution system's voltage stability, reliability and power loss (VRP) index. The objective function is tested on the modified IEEE-33 bus distribution system under three diverse scenarios with optimum sizing and placement of RESs and EVCS. To obtain optimal solutions for the proposed problem in a reasonable computation time, modified version of teaching and learning-based optimization (TLBO) and the JAYA algorithm are applied as the rate of convergence is superior to other existing methods in the literature and does not require any precise control parameters. For all the scenarios, it can be seen that the modified JAYA algorithm outperforms TLBO and other existing approaches. The findings of the results reveal the efficacy of uncertainty modelling in a proposed grid-connected DC MG to curtail the VRP index.

关键词： DC microgrid electric vehicle charging station JAYA algorithm teaching-learning based optimization uncertainties renewable energy sources

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Damage detection in a double-beam system using proper orthogonal decomposition and teaching-learning based algorithm

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SCIENTIA IRANICA 2020年第2期27卷 757-771页

作者： Mirzabeigy, A. Madoliat, R. Iran Univ Sci & Technol Sch Mech Engn Tehran 16846 Iran

This study applies the inverse approach to damage detection in a double-beam system. A double-beam system made of two parallel beams is connected through an elastic layer. Degradation of the stiffness of beams element, crack occurrence, and partial destruction of the inner layer are considered as different types of damage. The time domain acceleration response of the system is measured, and proper orthogonal decomposition is applied to the collected data in order to derive Proper Orthogonal Values (POVs) and Proper Orthogonal Modes (POMs) of the system. Effect of single damage in different locations on the POV has been analyzed, and an objective function is defined using the dominant POV and POM of each beam separately. In order to increase the robustness of the method against noise, the objective function is enriched by adding statistical property of time domain response. The teaching-learning based optimization algorithm is employed to solve the optimization problem. The efficiency of the proposed method for detecting single and multiple damages in the system is demonstrated with and without noise. Simulation results verify the good accuracy of the proposed method for detecting single and multiple damages of different types in the system. (C) 2020 Sharif University of Technology. All rights reserved.

关键词： Double-beam system Proper orthogonal decomposition Crack Damage teaching-learning based optimization

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A Game Theory Approach Using the TLBO Algorithm for Generation Expansion Planning by Applying Carbon Curtailment Policy

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ENERGIES 2022年第3期15卷 1172-1172页

作者： Mahvizani, Seyed Hamed Jalalzad Yektamoghadam, Hossein Haghighi, Rouzbeh Dehghani, Majid Nikoofard, Amirhossein Khosravy, Mahdi Senjyu, Tomonobu Sardar Jangal Univ Dept Engn Gilan *** Iran KN Toosi Univ Technol Dept Elect Engn Tehran *** Iran Amirkabir Univ Technol Dept Elect Engn Tehran *** Iran Cross Compass Ltd Cross Labs Tokyo 1040045 Japan Univ Ryukyus Fac Engn Okinawa 9030213 Japan

In the present climate, due to the cost of investments, pollutants of fossil fuel, and global warming, it seems rational to accept numerous potential benefits of optimal generation expansion planning. Generation expansion planning by regarding these goals and providing the best plan for the future of the power plants reinforces the idea that plants are capable of generating electricity in environmentally friendly circumstances, particularly by reducing greenhouse gas production. This paper has applied a teaching-learning-based optimization algorithm to provide an optimal strategy for power plants and the proposed algorithm has been compared with other optimization methods. Then the game theory approach is implemented to make a competitive situation among power plants. A combined algorithm has been developed to reach the Nash equilibrium point. Moreover, the government role has been considered in order to reduce carbon emission and achieve the green earth policies. Three scenarios have been regarded to evaluate the efficiency of the proposed method. Finally, sensitivity analysis has been applied, and then the simulation results have been discussed.

关键词： generation expansion planning teaching-learning based optimization game theory carbon emission

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A comparative study of different optimization algorithms for the optimum operation of the Mahabad dam reservoir

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RESULTS IN ENGINEERING 2024年 21卷

作者： Feizi, Hajar Sattari, Mohammad Taghi Apaydin, Halit Univ Tabriz Fac Agr Dept Water Engn Tabriz 51666 Iran Ankara Univ Fac Agr Dept Agr Engn TR-06110 Ankara Turkiye

In this study, the performance of the Differential Evolution (DE) Algorithm, the Genetic Algorithm (GA) and teaching-learning based optimization (TLBO) Algorithm are compared for the optimum operation of the Mahabad dam reservoir. The Reservoir supplies agricultural, environmental, municipal and industrial water demands of the area. The desktop reserve model is used for estimating the minimum environmental demand. Six alternative agricultural water management scenarios are proposed and a sensitivity analysis is performed on the agricultural demands under different scenarios. The results of the study indicate that both DE and GA algorithms performed nearly equally;however, the DE Algorithm reached its result more quickly with the reliability of 74.19 % compared to the results of the GA and TLBO. A 30 % decrease in agricultural demand in the fifth scenario with 87.56 % reliability provides the best results.

关键词： Optimal operation Differential evolution algorithm Genetic algorithm teaching-learning based optimization algorithm Environmental flow

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Secondary Control of Islanded Microgrids Using PI-Evolutionary Algorithms Under Uncertainties

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2019年第4期9卷 1827-1837页

作者： Rabeh, Reda Ferfra, Mohammed Ezbakhe, Ahmed Mohammed V Univ Rabat Ecole Mohammadia Ingenieurs Elect Engn Dept Res Team Power & Control EREEC Rabat Morocco Int Univ Rabat ECINE Lab LERMA Sala Al Jadida 11000 Morocco

Electrical grids converge now to a novel concept which named Microgrids (MG). it consists on producing energy in order to reduce dependency on fuel known by its fluctuant cost and to decrease harmful emission in the atmosphere. Constituted by renewable sources, energy storage system and controllable sources, a hybrid combination of these DERs is adopted to maintain MG reliability, transparency and efficiency of the whole system under deregulated power production. MG always are influenced by random weather conditions (e.g. temperature, solar radiation, wind speed...) of the non-controllable sources and load. These perturbations affect quality of energy especially frequency as a sensitive parameter to active power balance. To deal with this issue, a smart management of controllable sources is highly recommended to minimize frequency deviation. In this paper, a dynamic model is adopted with PI controller in controllable sources and storage system and presents a novel approach to design PI controller parameters by hybrid Evolutionary Algorithm (EA) GA-TLBO as a robust control of frequency regulation under uncertainty. A simulated isolated MG is tested on scenarios to validate the approach adopted in conceiving the PI parameters to avoid the frequency fluctuation in the different cases of study.

关键词： Isolated MG controlled sources noncontrolled sources frequency control PI controller evolutionary algorithm genetic algorithm teaching-learning based optimization

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An Improved Harmony Search Algorithm based on teaching-learning Strategy

An Improved Harmony Search Algorithm Based on Teaching-Learn...

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32nd Chinese Control Conference (CCC)

作者： Tuo Shouheng Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China

ISBN: (纸本)9789881563835

Harmony Search (HS) algorithm inspired by the music improvisation process is a swarm intelligent algorithm. In this paper, an improved global harmony search algorithm which is based on teaching-learning-based-optimization (TLBO) algorithm is proposed to solve the complex high-dimensional optimization problems. In the proposed algorithm, harmony memory consideration, teaching-learning strategy, local pitch adjusting and random operation method are adopted to improve the search ability of HS. The parameters are adjusted dynamically in terms of the process of iterations. Finally, the proposed algorithm is tested and compared with three other state-of-the-art HS optimization algorithms. The experimental results on 7 high-dimensional benchmark functions show that the HSTL method has strong convergence, and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.

关键词： Harmony Search Algorithm teaching-learning based optimization complex high-dimensional optimization problems

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