Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow

Ayan, Kürşat; Kılıç, Ulaş; Baraklı, Burhan

doi:10.1016/j.ijepes.2014.07.018

Cited by 61 publications

(36 citation statements)

References 25 publications

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“…Load shedding, considered as corrective action, is verified in various literatures [134][135][136][137][138]. Other various mathematical modelling and optimisation techniques include: PSO [139], decision trees [140][141][142][143][144][145], model predictive control [146], ant colony system [147][148][149], GA and ANN [150], differential evolution [151,152], and various other optimisation techniques [153,154].…”

Section: System Operationmentioning

confidence: 99%

Time‐horizons in the planning and operation of transmission networks: an overview

Khuntia

Tuinema

Rueda

et al. 2016

IET Generation, Transmission & Distribution

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In the planning and operation of power systems, actions are taken in different processes and time-horizons. The purpose of these actions is to secure a high reliability level. Although the three main processes (grid development, asset management, and system operation) are described in literature, there has been no explicit study on the time-horizons (long-term, mid-term, and short-term) and actual time-scale (decades, years, months, etc.) that these processes focus on. This study aims at making a review of the various activities performed by transmission system operators while reviewing the concept of each time-horizon and methodologies developed in literature. As decisions taken in different time-horizons can influence each other, the interactions and overlapping are discussed.

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Section: System Operationmentioning

confidence: 99%

Time‐horizons in the planning and operation of transmission networks: an overview

Khuntia

Tuinema

Rueda

et al. 2016

IET Generation, Transmission & Distribution

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“…These techniques can dominate many disadvantages of conventional techniques [4]. Several of these recent techniques have been applied to solve the OPF problem like: Simulated Annealing (SA) [5], Genetic Algorithm (GA) [6,7], Differential Evolution (DE) [8], Tabu Search (TS) [9], Imperialist Competitive Algorithm (ICA) [10], Particle Swarm Optimization (PSO) [11], adaptive real coded biogeography-based optimization (ARCBBO) [12], Biogeography Based Optimization (BBO) [13,14], multiphase search algorithm [15], Gbest guided artificial bee colony algorithm(Gbest-ABC) [16], Gravitational Search Algorithm (GSA) [17] , Artificial Bee Colony (ABC) [18], Multi-objective Grey Wolf Optimizer (MOGWO) [19], black-hole-based optimization (BHBO) [20], Teaching Learning based Optimization (TLBO) [21], Sine-Cosine Optimization algorithm (SCOA) [22], Group Search Optimization (GSO) [23], hybrid algorithm of particle swarm optimizer with grey wolves(PSO-GWO) [24], quasi-oppositional teaching-learning based optimization [31]have been incorporated into it. Meanwhile, many state-of-the-art meta-heuristic techniques, like Improved Colliding Bodies Optimization (ICBO) [32], Moth Swarm Algorithm (MSA) [33], Moth-Flame Optimization (MFO) [34], cuckoo search [35], firefly algorithm [36] and Backtracking Search Optimization Algorithm (BSA) [37] Surveys of different meta-heuristics used to solve the problem of OPF are offered in [25] The applications of these methods on different size systems lead to competitive results and therefore were favorable and encouraging for more study in this trend.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Method based on Krill Herd and Cuckoo Search for Optimal Power Flow Problem

Khelifi¹,

Bentouati²,

Chettih³

et al. 2019

IJIGSP

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Solving the Optimal power flow (OPF) problem is an urgent task for power system operators. It aims at finding the control variables' optimal scheduling subjected to several operational constraints to achieve certain economic, technical and environmental benefits. The OPF problem is mathematically expressed as a nonlinear optimization problem with contradictory objectives and subordinated to both constraints of equality and inequality. In this work, a new hybrid optimization technique, that integrates the merits of cuckoo search (CS) optimizer, is proposed to ameliorate the krill herd algorithm (KHA)'s poor efficiency. The proposed hybrid CS-KHA has been expanded for solving for single and multi-objective frameworks of the OPF problem through 11 case studies. The studied cases reflect various economic, technical and environmental requirements. These cases involve the following objectives: minimization of non-smooth generating fuel cost with valve-point loading effects, emission reduction, voltage stability enhancement and voltage profile improvement. The CS-KHA presents krill updating (KU) and krill abandoning (KA) operator derived from cuckoo search (CS) amid the procedure when the krill updating in order to extraordinarily improve its adequacy and dependability managing OPF problem. The viability of these improvements is examined on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus test system. The experimental results prove the greatest ability of the proposed hybrid meta-heuristic CS-KHA compared to other famous methods. Index Terms-Cuckoo search algorithm (CS); krill herd algorithm (KHA); optimal power flow (OPF); voltage stability (VS); valve-point effect; emission reduction. CS-KHA KHA CS Case.no Best Worst Mean Std dev Best Worst Mean Std dev Best Worst Mean Std dev Cace1 799.

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“…Yapıcı et al, [8] proposed a new solution using the firefly (FF) algorithm. Ayan et al, [9] offered a new hybrid approach by adapting chaos theory to the ABC algorithm. A local search called Nelder-Mead (NM) algorithm is integrated with SFLA in [10] numbered study.…”

Section: Introductionmentioning

confidence: 99%

New Metaheuristic Algorithms for Reactive Power Optimization

2019

Teh. vjesn.

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Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the powersystem control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms.

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Chaotic artificial bee colony algorithm based solution of security and transient stability constrained optimal power flow

Cited by 61 publications

References 25 publications

Time‐horizons in the planning and operation of transmission networks: an overview

Time‐horizons in the planning and operation of transmission networks: an overview

A Novel Hybrid Method based on Krill Herd and Cuckoo Search for Optimal Power Flow Problem

New Metaheuristic Algorithms for Reactive Power Optimization

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