Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

Baadji, Bousaadia; Bentarzi, Hamid; Bakdi, Azzeddine

doi:10.1080/0305215x.2019.1677635

Cited by 24 publications

(10 citation statements)

References 46 publications

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“…11 ), most of the BA applications under electrical and power system research domain. In this domain, the optimization problems addressed by BA are economic/emission dispatch [ 40 , 173 , 267 , 292 ], power economic dispatch [ 210 ], reactive power dispatch [ 209 , 214 ], optimal power flow [ 326 ], electronics, control, and communication [ 1 , 38 , 132 , 226 , 257 , 266 ], solar PV parameter estimation and solar radiation forecasting [ 3 , 310 , 337 ], photovoltaic energy system [ 76 , 94 – 96 , 145 , 176 , 194 , 225 , 259 ], forecasting of petroleum consumption [ 58 ], load frequency control [ 19 , 92 , 93 ], energy consumption [ 104 , 219 , 261 ], proportional-integral-derivative (PID) controller [ 60 , 122 , 164 , 187 , 188 , 193 , 224 , 233 , 330 ], wind speed forecasting [ 67 , 174 , 222 , 309 , 311 ], power system [ 30 , 31 , 36 , 70 , 81 , 91 , 106 , 121 , 162 , 258 , 278 , 284 ], dam–reservoir operation [ 99 ], and others [ 136 ]. These applications are presented in Fig.…”

Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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Section: Applications Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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“…Most of this research has been focused on the coordinated design of SVC and PSS controllers. For coordinated design of power system controllers, a large number of such algorithms have recently been offered, including Teaching-Learning Algorithm (TLA) [15], Bacterial Foraging Optimization (BFO) [16], Brainstorm Optimization Algorithm (BOA) [17], Coyote Optimization Algorithm (COA) [18], ant colony optimization (ACO) [19], bat algorithm (BAT) [20], bee colony algorithm (BCA) [11], Genetic Algorithm (GA) [21], particle swarm optimization (PSO) [22], flower pollination algorithm (FPA) [23], gravitational search algorithm (GSA) [24,25], sine-cosine algorithm (SCA) [26], grey wolf optimizer (GWO) [27], firefly algorithm (FA) [28], Differential Evolution (DE) [29], Biogeography-Based Optimization (BBO) [30], Cuckoo Search (CS) algorithm [31], Harmony Search (HS) [32], Seeker Optimization Algorithm (SOA) [33], Imperialist Competitive Algorithm (ICA) [34], Harris Hawk Optimization (HHO) [35], Sperm Swarm Optimization (SSO) [36], Tabu Search (TS) [37], Simulated Annealing [38], Multi-Verse Optimizer (MVO) [39], Moth-Flame Optimization (MFO) [40], and collective decision optimization (CDO) [41]. Although metaheuristic algorithms could provide relatively satisfactory results, no algorithm could provide superior performance than others in solving all optimizing problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Toolabi

Aghahadi

Eslami

et al. 2022

International Transactions on Electrical Energy Systems

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This paper presents a new approach for the coordinated design of a power system stabilizer- (PSS-) and static VAR compensator- (SVC-) based stabilizer. For this purpose, the design problem is considered as an optimization problem, while the decision variables are the controllers’ parameters. This paper proposes an effective optimization algorithm based on a rat swarm optimizer, namely, adaptive rat swarm optimization (ARSO), for solving complex optimization problems as well as coordinated design of controllers. In the proposed ARSO, instead of a random initial population, the algorithm starts the search process with fitter solutions using the concept of the opposite number. In addition, in each iteration of the optimization, the new algorithm replaces the worst solution with its opposite or a random part of the best solution to avoid getting trapped in local optima and increase the global search ability of the algorithm. The performance of the new ARSO is investigated using a set of benchmark test functions, and the results are compared with those of the standard RSO and some other methods from the literature. In addition, a case study from the literature is considered to evaluate the efficiency of the proposed ARSO for coordinated design of controllers in a power system. PSSs and additional SVC controllers are being considered to demonstrate the feasibility of the new technique. The numerical investigations show that the new approach may provide better optimal damping and outperform previous methods.

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“…A comprehensive study of the impacts of PSS and unified power flow controller (UPFC) for the enhancement of power system stability has been presented in [35]. In other studies, the optimal allocation of FACTS devices along with the optimal coordination between these devices and PSSs regulators has been investigated [36,37]. In [38] is proposed a new optimized interval type-II fuzzy set (T2FS) based on PSS to increase the stability margin of the well-known four-machine power system of a wind farm.…”

Section: Introductionmentioning

confidence: 99%

Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm

Kamarposhti

Çolak

Iwendi

et al. 2021

J CIRCUIT SYST COMP

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Volatility leads to disruption in synchronism between generators of a continuous system. The frequency of the volatility is usually between a few tenths of Hz to several Hz. This volatility is sometimes divided into two types, local and interregional. Local volatility is the low-frequency volatility of a power plant unit or units of a power plant relative to the grid whereas interregional volatility is the volatility of the units of one area relative to the units of another area. The worst kind of low-frequency volatility occurs when the power system in a region has a short three-phase connection to the earth, creating a complete instability of the grid and operating protective systems. One of the ways to improve the dynamic stability and steady-state of the power system is to use power system stabilizers and FACTS devices in the system. In this paper, the stabilization of the power system stabilizers (PSSs) and SSSC is done using the ant colony algorithm. Studies on a four-machine system with the three-phase error were performed in two scenarios and finally compared with the PSO method. The simulation results show that the proposed method produced more accurate performance.

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Comprehensive learning bat algorithm for optimal coordinated tuning of power system stabilizers and static VAR compensator in power systems

Cited by 24 publications

References 46 publications

Recent advances of bat-inspired algorithm, its versions and applications

Recent advances of bat-inspired algorithm, its versions and applications

Adaptive Rat Swarm Optimization for Optimum Tuning of SVC and PSS in a Power System

Optimal Coordination of PSS and SSSC Controllers in Power System Using Ant Colony Optimization Algorithm

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