Optimization of Reactive Power and Voltage Control in Power System Using Hybrid Artificial Neural Network and Particle Swarm Optimization

Kanata, Sabhan; Sianipar, Gibson H.M.; Maulidevi, Nur Ulfa

doi:10.1109/aemt.2018.8572408

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…Modified PSO [275], binary PSO [276] Reactive power and voltage control adaptive discrete binary PSO [277], standard PSO [278], PSO with artificial physics optimization [279], hybrid PSO [280] Short-Term load forecasting switching delayed PSO [281], hybrid PSO [282] VII. PSO DRAWBACKS Despite the excellent performance of its variants, PSO suffers, in general, from some weaknesses that can be alleviated by introducing new modifications to the current PSO variants.…”

Section: State Estimationmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Section: State Estimationmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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“…As it may be seen in equation (9), the next position of a moth is determined by the corresponding flame, and it is not necessarily in the space between them. Therefore, the exploration ability and the exploitation capacity of the population can be guaranteed.…”

Section: Mfo=(ipt) (6)mentioning

confidence: 99%

“…Swarm intelligence algorithm is a kind of nature-inspired stochastic algorithm for searching optimal solution of the nonlinear, non-differentiable and non-separable complex problem by simulating the foraging behaviors and biological habits of animals, and has received increasing attention in last several decades [1][2][3][4][5][6][7][8][9][10]. The classical swarm intelligence algorithms have Particle Swarm Optimization (PSO) [11], Artificial Bee Colony (ABC) [12] and Ant Colony Optimization (ACO) [13], etc.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Moth Flame Optimization Algorithm with Historical Flame Archive Strategy and its Applications

Wang

Cao

Jia

2022

Preprint

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The rule-based network intrusion detection system (IDS) is every effective and efficient for protecting the network equipment and avoiding suffering cyberattack. However, its rule generation is a NP-hard problem, which cannot be solved well by mathematical methods. Moth Flame Optimization (MFO) is a new nature-inspired heuristic algorithm, and has successfully been utilized to solve the various complex practical engineering problems. To effectively provide the rule of IDS, an adaptive MFO algorithm with historical flame archive strategy is proposed in this paper for enhancing exploitation of MFO and avoiding dropping into local optimal trap, which is termed MFO-HFA to avoid ambiguity. In MFO-HFA, to make effectively population escape local optimal trap, the archive consists of historical optimal individuals, which is utilized to preserve the information of better historical flame. Besides, to improve the convergence ability of the algorithm, a top flame randomly matching mechanism is utilized to make full use of the information of top flame. To demonstrate the advantage of MFO-HFA, it is compared with several well-known variants of MFO and some state-of-the-art intelligence algorithms on both 25 benchmark functions of CEC 2005. The experimental results indicate that MFO-HFA outperforms other compared algorithms and has obtained best accuracy. Furthermore, MFO-HFA is used to generate the rule of IDS by NSL-KDD dataset. The test results demonstrate that MFO-HFA outperforms compared algorithms and has gained 96.5% accuracy.

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“…The latest advancements in the power system are the implementation of artificial intelligence techniques with optimization techniques to attain voltage control. This topology has been implemented with PSO to solve the optimal reactive power dispatch problem (Kanata et al, 2018). The constraints involved in implementing the bio-inspired optimization technique with the artificial neural network technique are presented by discussing the system conditions of the grid (Kumar et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

Jaisiva,

Prabaakaran,

Kumar

et al. 2023

Front. Energy Res.

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This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods.

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Optimization of Reactive Power and Voltage Control in Power System Using Hybrid Artificial Neural Network and Particle Swarm Optimization

Cited by 7 publications

References 18 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

An Adaptive Moth Flame Optimization Algorithm with Historical Flame Archive Strategy and its Applications

A novel solution for the optimal reactive power dispatch problem using an artificial neural network integrated with the firefly optimization algorithm

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