Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A Novel Antillean Nighthawk Swarm Optimization Algorithm for Loss Lessening and Power Reliability Expansion

A Novel Antillean Nighthawk Swarm Optimization Algorithm for Loss Lessening and Power Reliability... This paper proposes Antillean nighthawk swarm optimization (ANSO) algorithm for loss lessening. Remarkable aims of the paper are Power reliability extension, power eccentricity minimization and loss lessening. The Antillean nighthawk swarm optimization (ANSO) algorithm is inspired by wisdom the activities of Antillean nighthawk in the swarm. Societal actions, collective communication, caricaturist nurturing, hovering, and watchfulness activities are applied for untangling the power loss lessening problem. Antillean nighthawk routinely voyages to diverse places; Antillean nighthawk can shift in creating and sponging once this is completed. The Antillean nighthawk with the uppermost nutrients reserve will act as creator, and Antillean nighthawk with bottommost rations will act as sponger. Antillean nighthawk with transitional comestibles would erratically pick to be as a creator and sponger Antillean nighthawk. In Flight Actions Antillean nighthawks might be move to alternative areas in rejoinder to marauder fears, scavenging, or several dissimilar conditions. As soon as Antillean nighthawks are repositioned to a dissimilar zone, the Antillean nighthawks will seek for nutrition over again. The creating Antillean nighthawk’s pursuit for nutrition spots, while the scroungers Antillean nighthawks shot to forage on the nutrition area which has been exposed by the creators Antillean nighthawks. Chaotic sequences are integrated in the Antillean nighthawk swarm optimization algorithm and it will augment the Exploration and Exploitation process. Quantum features are reliable pounded of median and it has been integrated with Antillean nighthawk swarm optimization algorithm. Opposition based learning integrated in the Antillean nighthawk swarm optimization algorithm and it applies the Laplace distribution to augment the exploration dexterity. Proposed Antillean nighthawk swarm optimization (ANSO) algorithm is corroborated in Garver’s 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system - WDN 220 KV (Unified Egyptian Transmission Network (UETN)). Loss lessening, power divergence curtailing, and power constancy index augmentation has been attained. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technology and Economics of Smart Grids and Sustainable Energy Springer Journals

A Novel Antillean Nighthawk Swarm Optimization Algorithm for Loss Lessening and Power Reliability Expansion

Download PDF
24 pages

Loading next page...
 
/lp/springer-journals/a-novel-antillean-nighthawk-swarm-optimization-algorithm-for-loss-asSjFcwpwx
Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022
eISSN
2199-4706
DOI
10.1007/s40866-022-00142-1
Publisher site
See Article on Publisher Site

Abstract

This paper proposes Antillean nighthawk swarm optimization (ANSO) algorithm for loss lessening. Remarkable aims of the paper are Power reliability extension, power eccentricity minimization and loss lessening. The Antillean nighthawk swarm optimization (ANSO) algorithm is inspired by wisdom the activities of Antillean nighthawk in the swarm. Societal actions, collective communication, caricaturist nurturing, hovering, and watchfulness activities are applied for untangling the power loss lessening problem. Antillean nighthawk routinely voyages to diverse places; Antillean nighthawk can shift in creating and sponging once this is completed. The Antillean nighthawk with the uppermost nutrients reserve will act as creator, and Antillean nighthawk with bottommost rations will act as sponger. Antillean nighthawk with transitional comestibles would erratically pick to be as a creator and sponger Antillean nighthawk. In Flight Actions Antillean nighthawks might be move to alternative areas in rejoinder to marauder fears, scavenging, or several dissimilar conditions. As soon as Antillean nighthawks are repositioned to a dissimilar zone, the Antillean nighthawks will seek for nutrition over again. The creating Antillean nighthawk’s pursuit for nutrition spots, while the scroungers Antillean nighthawks shot to forage on the nutrition area which has been exposed by the creators Antillean nighthawks. Chaotic sequences are integrated in the Antillean nighthawk swarm optimization algorithm and it will augment the Exploration and Exploitation process. Quantum features are reliable pounded of median and it has been integrated with Antillean nighthawk swarm optimization algorithm. Opposition based learning integrated in the Antillean nighthawk swarm optimization algorithm and it applies the Laplace distribution to augment the exploration dexterity. Proposed Antillean nighthawk swarm optimization (ANSO) algorithm is corroborated in Garver’s 6-bus test system, IEEE 30, 57, 118, 300, 354 bus test systems and Practical system - WDN 220 KV (Unified Egyptian Transmission Network (UETN)). Loss lessening, power divergence curtailing, and power constancy index augmentation has been attained.

Journal

Technology and Economics of Smart Grids and Sustainable Energy – Springer Journals

Published: Apr 28, 2022

Keywords: Optimization; Power; Transmission loss; Antillean nighthawk; Chaotic; Quantum; Opposition

References

  • A second-order cone programming reformulation of the economic dispatch problem of BESS for apparent power compensation in AC distribution networks
    Montoya, O; Gonzalez, W; Serra, F; Hernandez, J; Cabrera, A
  • Power stability analysis in medium-power distribution networks using a second-order cone approximation
    Montoya, O; Gonzalez, W; Londono, A; Rajagopalan, A; Hernandez, J
  • Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques: Status and technological review
    Saddique, MS; Bhatti, AR; Haroon, SS; Sattar, MK; Amin, S; Sajjad, IA; ul Haq, SS; Awan, AB; Rasheed, N
  • Finding solutions for optimal reactive power dispatch problem by a novel improved antlion optimization algorithm
    Li, Z; Cao, Y; Dai, LV; Yang, X; Nguyen, TT
  • Application of moth-flame optimizer and ant lion optimizer to solve optimal reactive power dispatch problems
    Mei, RNS; Sulaiman, MH; Daniyal, H; Mustaffa, Z
  • Solving optimal reactive power dispatch problem considering load uncertainty
    Kamel, S; Abdel-Fatah, S; Ebeed, M; Yu, J; Xie, K; Zhao, C
  • A novel algorithm for the Optimal Reactive Power Dispatch
    Das, T; Roy, R
  • Solution to optimal reactive power dispatch in transmission system using meta-heuristic techniques—status and technological review
    Saddique, MS; Bhatti, AR; Haroon, SS; Sattar, MK; Amin, S; Sajjad, IA; ul Haq, SS; Awan, AB; Rasheed, N
  • Hybrid PSO-tabu search for the optimal reactive power dispatch problem
    Sahli, Z; Hamouda, A; Bekrar, A; Trentesaux, D
  • Ant lion optimizer for solving optimal reactive power dispatch problem in power systems
    Mouassa, S; Bouktir, T; Salhi, A
  • Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization
    Mandal, B; Roy, PK
  • Optimal reactive power dispatch based on harmony search algorithm
    Khazali, H; Kalantar, M
  • Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm
    Tran, HV; Pham, TV; Pham, LH; Le, NT; Nguyen, TT
  • Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization
    Polprasert, J; Ongsakul, W; Dieu, VN
  • Automatic generation control of interconnected power systems using elephant herding optimization
    Dhillon, SS; Agarwal, S; Wang, G-G; Lather, JS
  • Harris hawks optimization with information exchange
    Qu, C; He, W; Peng, X; Peng, X
  • Seeker optimization algorithm for optimal reactive power dispatch
    Dai, C; Chen, W; Zhu, Y; Zhang, X
  • Optimal reactive power dispatch using self-adaptive real coded genetic algorithm
    Subbaraj, P; Rajnarayan, PN
  • Particle swarm optimization based optimal reactive power dispatch
    Pandya, S; Roy, R
  • Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch
    Hussain, AN; Abdullah, AA; Neda, OM
  • An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization
    Vishnu, M; Sunil
  • Impact of Simultaneous Allocation of Distributed Generation and UPFC in the Transmission Networks
    Mohanty, S; Samal, P; Patel, R; Behera, S; Mishra, S; Alam, MA
  • Optimal placement of FACTS devices in power transmission network using power stability index and fast power stability index
    Roshan, R; Samal, P; Sinha, P
  • Optimal STATCOM allocation and sizing using the sailfish optimizer algorithm
    Samal, P; Roshan, R
  • Modified lightning search algorithm for optimization
    Panigrahy, D; Samal, P
  • Implementation of Bio-Inspired Flower Pollination Algorithm in Distribution System Harmonic Mitigation Scheme
    Patel, R; Samal, P; Panda, AK; Guerrero, JM
  • A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation
    Samal, P; Ganguly, S; Mohanty, S
  • Modified shuffled frog leaping optimization algorithm based distributed generation rescheduling for loss minimization
    Arya, LD; Koshti, A
  • Solving dynamic economic emission dispatch problem with uncertainty of wind and load using whale optimization algorithm
    Padhi, S; Panigrahi, BP; Dash, D
  • Optimal placement of Facts device considering power stability and losses using teaching learning based optimization
    Verma, R; Rathore, A
  • Economic load dispatch using adaptive social acceleration constant based particle swarm optimization
    Jain, NK; Nangia, U; Jain, J
  • Power stability constrained distributed generation planning using modified bare bones particle swarm optimization
    Koshti, A; Arya, L; Choube, S
  • Class imbalance learning using UnderBagging based kernelized extreme learning machine
    Raghuwanshi, BS; Shukla, S
  • Multi-Objective Optimal Reactive Power Dispatch using Levy Interior Search Algorithm
    Nagarajan, K; Parvathy, AK; Arul, R
  • Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique
    Mei, RNS; Sulaiman, MH; Mustaffa, Z; Daniyal, H
  • Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer
    Nuaekaew, K; Artrit, P; Pholdee, N; Bureerat, S
  • Optimal reactive power dispatch based on harmony search algorithm
    Khazali, AH; Kalantar, M
  • Multi-objective enhanced PSO algorithm for optimizing power losses and power deviation in power systems
    Chen, G; Liu, L; Guo, Y; Huang, S
  • Chaotic Lévy flight bat algorithm for parameter estimation in nonlinear dynamic biological systems
    Lin, JH; Chou, CW; Yang, CH; Tsai, HL
  • A novel particle swarm optimization algorithm with levy flight
    Hakli, H; Uguz, H
  • Multi-objective optimal reactive power dispatch using levy interior search algorithm
    Nagarajan, K; Parvathy, AK; Arul, R
  • Towards complete detection of unstable periodic orbits in chaotic systems
    Davidchack, RL; Lai, YC; Klebanoff, A; Bolt, EM
  • Application of chaos degree to some dynamical systems
    Inoue, K; Ohya, M; Sato, K
  • Opposition-based krill herd algorithm with Cauchy mutation and position clamping
    Wang, G-G; Deb, S; Gandomi, AH; Alavi, AH
  • Transmission network expansion planning using a modified artificial bee colony algorithm
    Das, S; Verma, A; Bijwe, P
  • Power system transmission network expansion planning using AC model
    Rider, MJ; Garcia, AV; Romero, R
  • A new model for transmission network expansion and reactive power planning in a deregulated environment
    Mahmoudabadi, A; Rashidinejad, M; Maymand, MZ
  • Transmission expansion planning with AC model based on generalized benders decomposition
    Asadamongkol, S; Eua-arporn, B
  • Techno-economic based static and dynamic transmission network expansion planning using improved binary bat algorithm
    Mouwafi, MT; Abou El-Ela, AA; El-Sehiemy, RA; Al-Zahar, WK
  • Optimal Transmission System Expansion Planning Via Binary Bat Algorithm
    Abou El-Ela, AA; Mouwafi, MT; Al-Zahar, WK
  • Quantum-inspired genetic algorithm based on phase encoding
    Liu, X; Liu, X
  • Quantum discord for two-qubit systems
    Luo, S
  • Quantum machine learning in feature hilbert spaces
    Schuld, M; Killoran, N