Disparity Line Utilization Factor and Galaxy-based Search Algorithm for Advanced Congestion Management in Power Systems

Mishra, Akanksha; Nagesh, Venkata; Gundavarapu, Kumar

doi:10.14716/ijtech.v7i1.1556

Cited by 2 publications

(4 citation statements)

References 11 publications

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“…The authors in [27] have been explored a new heuristic optimization algorithm which has the name GBSA for the appropriate location and setting of the IPFC based on the DLUF index to optimize a multi-objective functions containing the reduction of total voltage deviations at load buses, the minimization of transmission loss and the security margin. A test system IEEE 30-bus has been applied to ascertain the ability and accuracy of this employed technique compared to GA under different loading conditions.…”

Section: Gbsa Techniquementioning

confidence: 99%

“…These methods are easier to find the best solution to problems than traditional methods. This group can be classified into four categories [18], (i)Evolutionary algorithms like Genetic Algorithms (GA) [19], Evolution Strategy (ES) [20], Evolutionary Programming (EP) [21], Genetic Programming (GP) [22], (ii) Physics-based algorithms contain Ant Lion Optimization (ALO) technique [23], Biogeography Based Optimizer (BBO) [24], Curved Space Optimization (CuSO) [25], Flower Pollination Algorithm (FPA) [26], Galaxy-based Search Algorithm (GBSA) [27], Gravitational Search Algorithm (GSA) [28], Harmony Search Algorithm (HAS) [29], Multi-Verse Optimization (MVO) Algorithm [30], Simulated Annealing (SA) [31], Atom Search Optimization (ASO) Algorithm [32], etc. (iii) Swarm Based algorithms such as Particle Swarm Optimization (PSO) [33], Whale optimization algorithm (WOA) [34], Artificial Bee Colony (ABC) [35], Chemical Reaction Optimization (CRO) algorithm [36], Crow Search Algorithm (CSA) [37], Cat Swarm Optimization (CaSO) algorithm [38], Cuckoo search (CS) [39], Dragonfly Algorithm (DA) [40], Bats Algorithm (BA) [41], Firefly algorithm (FFA) [42], Grasshopper optimization algorithm (GOA) [43], Grey Wolf Optimizer (GWO) [44], Honey-Bee Mating Optimization (HBMO) [45], Moth-Flame Optimization (MFO) algorithm [46], Bacterial Swarm Optimization (BSO) [47], Immune Algorithm (IA) [48], Symbiotic Organism Search (SOS) Algorithm [49], etc.…”

Section: Introductionmentioning

confidence: 99%

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Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

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Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

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Section: Gbsa Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Ismail¹,

Guesmi²,

Alshammari³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors in ref. [27] explored a new heuristic optimization algorithm named GBSA for identifying the most appropriate location and settings of the IPFC based on the DLUF index to optimize multi-objective functions to reduce the total voltage deviations at load buses and minimize transmission losses and the security margin. A test system IEEE 30-bus was applied to ascertain the ability and accuracy of this employed technique compared to GA under different loading conditions.…”

Section: • Gbsa Techniquementioning

confidence: 99%

“…These methods are easier to use when determining the best solution to problems than traditional methods. This group can be classified into four categories [18]: (i) evolutionary algorithms such as genetic algorithms (GA) [19], evolution strategy (ES) [20], evolutionary programming (EP) [21], genetic programming (GP) [22]; (ii) physics-based algorithms such as the ant lion optimization (ALO) technique [23], biogeography-based optimizer (BBO) [24], curved space optimization (CuSO) [25], flower pollination algorithm (FPA) [26], galaxy-based search algorithm (GBSA) [27], gravitational search algorithm (GSA) [28], harmony search algorithm (HAS) [29], multiverse optimization (MVO) algorithm [30], simulated annealing (SA) [31], atom search optimization (ASO) algorithm [32]; (iii) swarm-based algorithms such as particle swarm optimization (PSO) [33], whale optimization algorithm (WOA) [34], artificial bee colony (ABC) [35], chemical reaction optimization (CRO) algorithm [36], crow search algorithm (CSA) [37], cat swarm optimization (CaSO) algorithm [38], cuckoo search (CS) [39], dragonfly algorithm (DA) [40], bats algorithm (BA) [41], firefly algorithm (FFA) [42], grasshopper optimization algorithm (GOA) [43], gray wolf optimizer (GWO) [44], honey-bee mating optimization (HBMO) [45], moth-flame optimization (MFO) algorithm [46], bacterial swarm optimization (BSO) [47], immune algorithm (IA) [48], symbiotic organism search (SOS) algorithm [49], etc. ; and (iv) other population-based algorithms such as the black hole (BH) algorithm [50], parallel seeker optimization algorithm (PSOA) [51], impe...…”

mentioning

confidence: 99%

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

et al. 2023

View full text Add to dashboard Cite

The use of FACTS devices in power systems has become increasingly popular in recent years, as they offer a number of benefits, including improved voltage profile, reduced power losses, and increased system reliability and safety. However, determining the optimal type, location, and size of FACTS devices can be a challenging optimization problem, as it involves mixed integer, nonlinear, and nonconvex constraints. To address this issue, researchers have applied various optimization techniques to determine the optimal configuration of FACTS devices in power systems. The paper provides an in-depth and comprehensive review of the various optimization techniques that have been used in published works in this field. The review classifies the optimization techniques into four main groups: classical optimization techniques, metaheuristic methods, analytic methods, and mixed or hybrid methods. Classical optimization techniques are conventional optimization approaches that are widely used in optimization problems. Metaheuristic methods are stochastic search algorithms that can be effective for nonconvex constraints. Analytic methods involve sensitivity analysis and gradient-based optimization techniques. Mixed or hybrid methods combine different optimization techniques to improve the solution quality. The paper also provides a performance comparison of these different optimization techniques, which can be useful in selecting an appropriate method for a specific problem. Finally, the paper offers some advice for future research in this field, such as developing new optimization techniques that can handle the complexity of the optimization problem and incorporating uncertainties into the optimization model. Overall, the paper provides a valuable resource for researchers and practitioners in the field of power systems optimization, as it summarizes the various optimization techniques that have been used to solve the FACTS optimization problem and provides insights into their performance and applicability.

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Disparity Line Utilization Factor and Galaxy-based Search Algorithm for Advanced Congestion Management in Power Systems

Cited by 2 publications

References 11 publications

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

Optimized FACTS Devices for Power System Enhancement: Applications and Solving Methods

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