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A comparative study on the computation of directional overcurrent relay coordination in power systems using PSO and TLBO based optimization

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ENGINEERING COMPUTATIONS 2016年第2期33卷 603-621页

作者： Saha, Debasree Datta, Asim Roy, Biman Kumar Saha Das, Priyanath Natl Inst Technol Agartala Dept Elect Engn Jirania India Natl Inst Technol Meghalaya Dept Elect Engn Shillong Meghalaya India

Purpose - Directional Overcurrent Relay (DOCR) coordination computation allowing for desired and high-level accuracy in interconnected power systems is very difficult and is a highly constraint-oriented optimization problem. The purpose of this paper is to study the effectiveness of a newly reported optimization technique, teaching learning based optimization (TLBO), in protective relay coordination comparing with a widely used optimization technique, Particle Swarm optimization (PSO). Design/methodology/approach - DOCR coordination in electric power systems is considered as an optimization problem by formulating objective function and specifying problem constraints. Optimum values of the DOCR adjustment parameters (time dial setting and plug setting) in terms of reliable coordination margin and operating times of relays are computed by both the algorithms, TLBO and PSO. Optimal coordination is verified in three test bus systems: IEEE 6-bus, Western System Coordinating Council 9-bus and IEEE 14-bus systems. Findings - A comparison between the numerical results of using both the algorithms indicates that the TLBO gives better results in terms of the total operating times of relays and coordination time interval. Originality/value - This paper represents the performance of a newly reported optimization technique, TLBO which is till now unpopular to protection engineers to be applied in protective relay coordination applications. The technique provides better performance in comparison to the widely applied technique, PSO. It is expected that TLBO would facilitate protection engineers to decide the optimum and appropriate settings of the relays for leading exact relays coordination.

关键词： teaching learning based optimization Particle swarm optimization Comparative study Directional overcurrent relay Relay coordination

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Forecasting gas density using artificial intelligence

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PETROLEUM SCIENCE AND TECHNOLOGY 2017年第9期35卷 903-909页

作者： Choubineh, Abouzar Khalafi, Elias Kharrat, Riyaz Bahreini, Alireza Hosseini, Amir Hossein Petr Univ Technol Petr Dept Ahvaz Iran Shiraz Univ Petr Dept Shiraz Iran Semnan Univ Petr Dept Semnan Iran

Proper calculations of gas engineering require precise determination of gas properties and its associated variations with pressure and temperature. These properties can be determined by conducting experimental tests on gathered fluid samples from the bottom of the wellbore or at the surface as well as using equations of state and empirical correlations. This work is concentrated to develop a robust and quick model based on artificial network trained with teaching learning based optimization (ANN-TLBO) using 693 data sets at a wide range of pressure and temperature for gas density prediction. Comparing gas density from the predictive method and experimental results describe that the proposed ANN-TLBO model is of reliable accuracy for determining gas density. Sensitivity analysis also showed the extreme effect of temperature and pressure on gas density.

关键词： Artificial neural network comparing gas density sensitivity analysis teaching learning based optimization

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teaching learning based algorithm for OPF with DC link placement problem

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 773-781页

作者： Babu, B. Suresh Palaniswami, S. Odaiyappa Coll Engg & Tech Elect & Elect Engn Theni 625531 Tamil Nadu India Govt Coll Engn Elect & Elect Engn Bodinayakkanur 625582 Tamil Nadu India

Optimal Power Flow (OPF) is an important operational and planning problem in niinimizing the chosen objectives of the power system. The recent developments in power electronics allow replacing the existing transmission lines by DC links with a view of making the operation more flexible, secure and economical. This paper formulates a new DC link placement problem through extending the standard OPF to embrace DC link equations and presents a heuristic optimization technique, inspired from the teaching learning process in class rooms, for solving the problem. The solution process involves sequential NR based AC/DC power flow. It presents simulation results of two IEEE test systems with a view of demonstrating its effectiveness. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Optimal power flow AC/DC power flow teaching learning based optimization

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Enhanced control of doubly fed induction generator based wind turbine system using soft computing assisted fractional order controller

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RENEWABLE ENERGY FOCUS 2022年 43卷 291-308页

作者： Karad, Shivaji Ganpat Thakur, Ritula Natl Inst Tech Teachers Training & Res Chandigarh 160019 India Natl Inst Tech Teachers Training & Res Sect 26 Chandigarh India

This paper proposes teaching learning based optimization (TLBO) algorithm assisted fractional order con-troller for control of Rotor side converter (RSC) and Grid side converter (GSC) for efficient and optimal control of doubly fed induction machine based wind turbine (WT) system. The proposed fractional order proportional integral (FOPI) controller is tuned using Genetic algorithm (GA), particle swarm optimiza-tion (PSO) and TLBO techniques. The rotating reference frame with stator flux alignment dependent field oriented vector control scheme is accomplished for decoupled optimal power control. Proposed DFIG based WT system implemented and simulated using MATLAB/Simulink. Simulation results of proposed TLBO based fractional order control are compared with GA and PSO tuned FOPI controller. The simulation results obtained are expedient that TLBO tuned FOPI is more prominent in DC link voltage control and shown promising results as compared to GA and PSO tuned FOPI Controller in terms of the of the solution time required and time domain performance parameters.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Fractional order control Genetic algorithm Particle swarm optimization Doubly fed induction generator teaching learning based optimization

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Hybrid algorithm DE-TLBO for optimal H-a and PID control for multi-machine power system

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2017年第2期8卷 925-936页

作者： Dib, Faiza Boumhidi, Ismail Univ Sidi Mohamed Ben Abdellah LESSI Lab Fac Sci Dhar El Mehraz Fes Morocco

This paper propose, a robust excitation controller designed by a coordination of the optimal H-a tracking control and the proportional integral derivative (PID) controller optimized by the hybrid differential evolution and teaching-learning based optimization algorithm (DE-TLBO). These two controllers are used in order to guarantee the transient stability during a change in the operating conditions and the uncertainties in parameters. We have applied a method based on the modified tracking error by using the optimized exponential function, to avoid the compromise between the high gain in the control input and the H-infinity tracking performance with the variation in the system parameter. A new hybrid algorithm (DE-TLBO) is employed in this study to adjust optimally the parameters of the (PID-PSS) controller and the exponential form of the tracking error modified. The purpose of the suggested approach is to ensure a good tracking accuracy and to enhance the level of the oscillations damping in the multi-machine power system with an optimal choice of the parameters of all proposed controllers. The results of simulation demonstrate the efficient, and the robustness of the proposed approach (H-infinity and DE-TLBO-PID-PSS) under the different operation conditions.

关键词： Differential evolution teaching learning based optimization H-infinity tracking control Multi-machine power system Proportional integral derivative

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Application of TLBO for Distribution Network Planning via Coordination of Distributed Generation and Network Reconfiguration

Application of TLBO for Distribution Network Planning via Co...

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9th IFAC Symposium on Control of Power and Energy Systems (CPES 2015)

作者： Kanwar, Neeraj Gupta, Nikhil Niazi, K. R. Swarnkar, Anil Bansal, R. C. Malaviya Natl Inst Technol Dept Elect Engn Jaipur 302017 Rajasthan India Univ Pretoria Dept Elect Elect & Comp Engn ZA-0002 Pretoria South Africa

This paper proposes an application of teaching learning based optimization (TLBO) method for optimal integration of Distributed Generations (DGs) in distribution systems to minimize annual energy loss and to maintain a better node voltage profile using penalty factor approach. A piece-wise linear variable load pattern is considered and the distribution network is reconfigured after optimal DG placement. Efforts have been made to scan the search space efficiently in such a way that every node of the system is a candidate node for DG placement with a priority level. The proposed method is applied on IEEE 33-bus test distribution system. The results obtained are promising when compared with other recently established methods. (C) 2015, IFAC (International Federation of Automatic Control) Hosting, by Elsevier Ltd. All rights reserved.

关键词： Distributed generations intelligent search network reconfiguration smart distribution systems teaching learning based optimization

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Swarm Intelligence Algorithm for Microwave Filter optimization 6th

Swarm Intelligence Algorithm for Microwave Filter Optimizati...

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6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA)

作者： Chahrazad, Erredir Bouarroudj, Emir Riabi, Mohamed Lahdi Univ Bros Mentouri Constantine 1 Lab Electromagnet & Telecommun Constantine Algeria

ISBN: (纸本)9783319897431;9783319897424

In this paper, three recent swarm intelligence algorithms (spider monkey optimization (SMO), social spider optimization (SSO) and teaching learning based optimization (TLBO)) are proposed to the optimization of microwave filter (H-plane three-cavity filter). The results of convergence and optimization use of these algorithms are compared with the results of the most popular swarm intelligences algorithm, namely particle swarm optimization (PSO) for different common parameters (population size and maximum number of iteration). The results showed validation of the proposed algorithms.

关键词： Swarm intelligence Spider monkey optimization Social spider optimization teaching learning based optimization Particle swarm optimization Microwave filter

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Design and Implementation of an Enhanced Demand-Side Management Solution For Distribution End Prosumers

Design and Implementation of an Enhanced Demand-Side Managem...

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IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)

作者： Pati, Uttamarani Mistry, Khyati D. Sardar Vallabhbhai Natl Inst Technol Elect Engn Dept Surat India

ISBN: (纸本)9798350332117

Power industry is presently undergoing tremendous revolutionary changes in order to move towards a decentralized power system network. The rising concerns about the contribution of greenhouse gases and carbon emissions to global warming have led utilities to opt for carbon-free green energy resources. This paper presents an effective load management option for the end users where the teaching-learning optimization-based demand-side management technique and integration of photovoltaic (PV) generation come into the frame. It also focuses on building a smooth, secure and flexible trading platform between the prosumers and the utility. The proposed methodology has been tested on a dataset containing load consumption and PV generation details of 50 prosumers. The methodology proposed has been designed using MATLAB 2020a and the Remix IDE interface.

关键词： teaching learning based optimization Demand-Side Management Photo-Voltaic Generation Blockchain-based Trading System

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Multi-objective optimization for energy management of fuel cell hybrid electric vehicles

Multi-objective optimization for energy management of fuel c...

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American Control Conference

作者： Liu, Hao Chen, Jian Wu, Chengshuai Chen, Hao Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781538654286

Energy management strategies (EMSs) are crucial for enhancing the economy and durability of fuel cell hybrid electric vehicles (FCHEVs). However, most existing EMSs are only concerned with economy and ignore the durability, especially the durability of the energy sources. In this paper, based on an improved FCHEV powertrain model, an equivalent cost function considering both economy and durability is developed, and then a fuzzy logic based EMS is designend to realize the energy distribution between fuel cell and battery. After that, based on three different driving cycles, the teaching learning based optimization (TLBO) is introduced to solve the parameters optimization problem of the fuzzy logic based multi objective EMS. Test results show that the TLBO algorithm has faster convergence rate and better optimization results than the existing genetic algorithm approach.

关键词： Energy management strategy multi-objective optimization teaching learning based optimization fuel cell hybrid electric vehicles

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Automatic Generation Control of a two area multi-unit interconnected power system with Proportional-Integral-Derivative controller with Filter(PIDF) optimized by TLBO algorithm

Automatic Generation Control of a two area multi-unit interc...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

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

ISBN: (纸本)9781509012770

This article describes the automatic generation control of a two area multi-unit interconnected power system with Proportional-Integral-Derivative controller with Filter (PIDF) tuned by teaching learning based optimization (TLBO) algorithm. Each area is equipped with three reheat thermal units with hydraulic amplifiers. The gains of the PIDF controllers are simultaneously optimized by applying a step load perturbation of 0.01 in area 1 considering integral time absolute error (ITAE) as the fitness function. The enhancement of the control behavior of the proposed optimized PIDF controller is proved in comparison with the conventional PID controller. The dynamic responses of the system is compared in terms of settling time, peak overshoots and undershoots. Further the sensitiveness and robustness of system is verified by applying disturbances in both the area simultaneously.

关键词： Automatic Generation Control teaching learning based optimization Proportional-Integral-Derivative controller with Filter Integral Time Absolute Error

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