scholarly journals Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2297 ◽  
Author(s):  
Wadood ◽  
Khurshaid ◽  
Farkoush ◽  
Yu ◽  
Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Operating Time ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Test Systems ◽  
Whale Optimization ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Hunting Strategy

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.

DEVELOPMENT OF BONOBO ALGORITHM AND ITS APPLICATION FOR OPTIMAL COORDINATION OF DIRECTIONAL OVERCURRENT RELAYS IN POWER SYSTEMS

10.6036/10060 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 492-497
Author(s):  
MOHAMMED ABDELHAMID ◽  
SALAH KAMEL ◽  
ALI SELIM ◽  
MOHAMED MOHAMED ◽  
MAHDROUS AMED ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Operating Time ◽  
Lévy Flight ◽  
Levy Flight ◽  
System Protection ◽  
Total Operating Time ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Operational Constraints

In this paper, an improved Bonobo optimization algorithm (IBO) is proposed to solve the optimal coordination of directional overcurrent relays (DOCRs) problem. This problem is important for power system protection. It is considered a nonlinear and highly constrained optimization problem. IBO aims to improve the performance of the original Bonobo optimization algorithm (BO) using Levy flight distribution and three leaders selection. Both BO and IBO are utilized to develop two solvers for optimal coordination of DOCRs. The 15-bus and 30-bus test systems are used to validate BO and IBO in minimizing the total operating time of relays with satisfying the operational constraints. The results of the proposed IBO algorithm have been compared with the original BO algorithm and other well-known algorithms. The obtained results confirmed the effectiveness and superiority of the proposed IBO algorithm compared with the other algorithms in minimizing the total operating time of relays for the optimal coordination of DOCRs. Keywords: Directional overcurrent relays; Optimal coordination; Improved Bonobo algorithm; Levy flight distribution; Three leaders selection.

scholarly journals Whale Optimization Algorithm-Based DG Allotment for Loss Minimization of Distribution Networks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Power Systems ◽  
Radial Distribution ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Loss Minimization ◽  
Optimum Placement ◽  
Suggested Technique ◽  
Whale Optimization

For optimum placement of distributed generation (DG) units in balanced radial distribution network for loss minimization, implementation of whale optimization algorithm (WOA), a state-of-the-art meta-heuristic optimization algorithm is proposed in this paper. Encouraged by bubble-net hunting strategy of whales, WOA mimes the collective practice of humpback whales. For validating performance in solving the mentioned problem, the suggested technique is implemented on IEEE 33-bus and IEEE 69-bus balanced radial distribution test networks. The obtained results demonstrate that feasible and effective solutions are obtained using the proposed approach and can be used as a propitious substitute in practical power systems to overcome the optimum DG siting and sizing issue. Also concerning the best knowledge of the authors, it is the first report on the application of WOA in solving optimum DG siting and sizing issue.

scholarly journals An Improved Optimal Solution for the Directional Overcurrent Relays Coordination Using Hybridized Whale Optimization Algorithm in Complex Power Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 90418-90435 ◽  
Author(s):  
Tahir Khurshaid ◽  
Abdul Wadood ◽  
Saeid Gholami Farkoush ◽  
Jiangtao Yu ◽  
Chang-Hwan Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Whale Optimization ◽  
Complex Power ◽  
Overcurrent Relays

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

scholarly journals Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kun-Chou Lee ◽  
Pai-Ting Lu
Keyword(s):  
Inverse Scattering ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Hunting Behavior ◽  
Whale Optimization ◽  
The Moment

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.

scholarly journals Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

2020 ◽  
Vol 5 (3) ◽  
pp. 147-155
Author(s):  
I-Ming Chao ◽  
Shou-Cheng Hsiung ◽  
Jenn-Long Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Position Vector ◽  
Hunting Behavior ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Solution Accuracy

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

scholarly journals Optimal Reconfiguration of Electrical Distribution System by Whale Optimization Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 2392-2398
Keyword(s):  
Optimization Algorithm ◽  
Distribution System ◽  
Electrical Power ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Voltage Profile ◽  
Electrical Power System ◽  
Electrical Distribution ◽  
Whale Optimization ◽  
Optimal Reconfiguration

The prime motto of the electrical power system is to provide the good and high quality power to the consumers. As the life in the society is expanding hugely, hence the need of the electrical power is additionally expanding suggestively. In this manner expanding the power generation as well as beating the significant issues in the electrical distribution system has turned into a test. The strange conditions can't be normal however when happened; the recuperation ought to be made as quickly as time permits. In this work, a modern artificial intelligence based algorithm is implemented for the reconfiguration of an electrical radial distribution network. This algorithm helps to bring down the active power loss and intensify the voltage profile of the network. This paper has proposed a nature-based guided metaheuristic Whale Optimization Algorithm (WOA). WOA is motivated by the smart foraging approach of the humpback whales. To ratify the efficiency of the proposed approach, WOA is successfully simulated on IEEE standard 69 bus and 119 bus system.

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