Cost, emission and reserve pondered pre‐dispatch of thermal power generating units coordinated with real coded grey wolf optimisation

Rameshkumar, J.; Ganesan, S.; Abirami, M.; Subramanian, S.

doi:10.1049/iet-gtd.2015.0726

Cited by 20 publications

(21 citation statements)

References 37 publications

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“…The robustness value of all individuals of the present candidate solution matrix (X o ) is computed by Eq. (31). The robustness of i th individual represents its (wolf's) distance from the prey.…”

Section: -4: Evaluation Of Objective Value and Finding Best Positionmentioning

confidence: 99%

“…Sort the population based on the robustness from minimum to maximum, an individual having the minimum robustness is imitated as the alpha; second and third minimum is beta and delta respectively. fiti=objective value (31) Step…”

Section: -4: Evaluation Of Objective Value and Finding Best Positionmentioning

confidence: 99%

“…First, it has benchmarked on twentynine standard test functions, and the results are validated by comparing with other methods [29]. Later, it was applied for solving economic load dispatch problem [30], and followed its convergence characteristic and performance has been investigated while solving the unit commitment problem [31].…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

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A novel metaheuristic optimization procedure for optimizing electrical distribution system is proposed that may help the system planners to know the minimum and maximum objective values. In which the optimal distribution network reconfiguration (DNR) framework and implementation of modified grey wolf optimization (mGWO) algorithm to minimize power loss reduction and node voltage deviation indices have delineated. The DNR involves nonlinear and multimodal function that has been optimized under practical constraints. For the purpose, mGWO algorithm is employed for ascertaining optimum switching position while reconfiguring the distribution system at a minimum fitness value. In fact grey wolf's update its position linearly from a higher value to zero in the search vicinity that provides perfect balance among intensification and diversification to ascertain the best fitness function and exhibits quick and steady convergence. Moreover, the proposed method seems to be a promising optimization tool for the electrical utilities, thereby modifying their operating strategy of distribution system under steady state conditions. This study is conducted on standard IEEE 33-bus and 69-bus distribution systems, the simulated results have analyzed and compared with several recent methods. It shows that the numerical results have provided, by mGWO is superior among the contestant algorithms.

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Section: -4: Evaluation Of Objective Value and Finding Best Positionmentioning

confidence: 99%

Section: -4: Evaluation Of Objective Value and Finding Best Positionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Nataraj¹,

Loganathan²,

Veerasamy³

et al. 2018

IJIES

View full text Add to dashboard Cite

show abstract

“…The five resources are R= (R1, R2, R3, R4, R5). The aim of proposed work is reducing the objective function using equation (10) and (14). In this, we design the Data size (DS) value of each subtask which is given in table 3.…”

Section:  Example 1: a Five Task And Five Resourcesmentioning

confidence: 99%

“…In recent years, scheduling approach plays a significant role in modern applications and especially, task scheduling has been received a great transaction of attention among the studies due to its wide applicability and abundant growth of cloud computing based system [14]. A good scheduler familiarizes its scheduling approach according to the altering environment and the type of task.…”

Section: Introductionmentioning

confidence: 99%

Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment

Natesan¹,

Chokkalingam²

2017

IJIES

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Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.

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A fuzzy reinforcement learning approach to thermal unit commitment problem

Navin

Sharma

2017

Neural Comput & Applic

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Cost, emission and reserve pondered pre‐dispatch of thermal power generating units coordinated with real coded grey wolf optimisation

Cited by 20 publications

References 37 publications

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Optimizing Radial Distribution System for Minimizing Loss Reduction and Voltage Deviation Indices Using Modified Grey Wolf’s Algorithm

Opposition Learning-Based Grey Wolf Optimizer Algorithm for Parallel Machine Scheduling in Cloud Environment

A fuzzy reinforcement learning approach to thermal unit commitment problem

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