Anticipatory load shedding for line overload alleviation using Teaching learning based optimization (TLBO)

Arya, L. D.; Koshti, Atul

doi:10.1016/j.ijepes.2014.06.066

Cited by 33 publications

(19 citation statements)

References 23 publications

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“…The TLBO algorithm, described in Algorithm 3, is a two-phase algorithm; teacher phase and learner phase. It has been proven effective in solving various engineering problems [70][71][72][73][74][75][76].…”

Section: Preliminariesmentioning

confidence: 99%

Settings-Free Hybrid Metaheuristic General Optimization Methods

et al. 2020

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Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

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Section: Preliminariesmentioning

confidence: 99%

Settings-Free Hybrid Metaheuristic General Optimization Methods

et al. 2020

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“…The load shedding is usually used as a control measure to help the power system alleviate its overload [20]. In [21], a load shedding algorithm was proposed for alleviating overload in transmission lines by employing the teaching learning-based optimization, which determines the optimal load shedding at selected buses based on the sensitivity of severity index. A load shedding strategy for overload relief in transmission network was proposed in [22], which involves real-time dynamic thermal line rating technology into the conventional load shedding strategies.…”

Section:  Load Sheddingmentioning

confidence: 99%

Alleviation of overloads in transmission network: A multi-level framework using the capability from active distribution network

Jin

Jia

et al. 2019

International Journal of Electrical Power & Energy Systems

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“…In particular, the Teaching-learning-based optimization (TLBO) algorithm, presented in [2], has proven its effectiveness in a wide range of applications. For example in [3], it is used for the optimal coordination of directional overcurrent relays in a looped power system; in [4], a multi-objective TLBO is used to solve the optimal location of automatic voltage regulators in distribution systems in the presence of distributed generators; in [5], an improved multi-objective TLBO is applied to optimize an assembly line to produce large-sized high-volume products such as cars, trucks and engineering machinery; in [6], a load shedding algorithm for alleviating line overloads employs a TLBO algorithm; in [7], a TLBO algorithm is used to optimize feedback gains and the switching vector of an output feedback sliding mode controller for a multi area multi-source interconnected power system; in [8], the TLBO method is used to train and accelerate the learning rate of a model designed to forecast both wind power generation in Ireland and that of a single wind farm, in order to demonstrate the effectiveness of the proposed method; in [9] Cetane number estimation of biodiesel with a fatty acid methyl esters composition was performed using a hybrid optimization method including a TLBO algorithm; in [10], a residential demand side management scheme based on electricity cost and peak to average ratio alleviation with maximum user satisfaction is proposed using a hybrid technique based on TLBO and enhanced differential evolution (EDE) algorithms; in [11] a TLBO algorithm is used in Transmission Expansion Planning (TEP) that involves determining if and how transmission lines should be added to the power grid, considering power generation costs, power loss, and line construction costs among others.…”

Section: Introductionmentioning

confidence: 99%

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

et al. 2018

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A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.

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Anticipatory load shedding for line overload alleviation using Teaching learning based optimization (TLBO)

Cited by 33 publications

References 23 publications

Settings-Free Hybrid Metaheuristic General Optimization Methods

Settings-Free Hybrid Metaheuristic General Optimization Methods

Alleviation of overloads in transmission network: A multi-level framework using the capability from active distribution network

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

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