Optimal placement and sizing of multiple active power filters for radial distribution system using grey wolf optimizer

Lakum, Ashokkumar; Mahajan, Vasundhara

doi:10.1109/icpes.2017.8387357

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Four further publications [118][119][120]150] have presented the properties of the grey wolf optimizer (GWO) algorithm. It is based on the social hierarchy, encircling prey, hunting, exploitation, and exploration of grey wolves.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

“…As a test system, the authors used a 33-node radial system in [118][119][120], and a 69-node system in [150]. The last article concerned the properties of the GFO algorithm in its adaptive version (AGFO), which could be used in the event of a variable level of disturbance content, e.g., in systems containing distributed renewable energy generation systems.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

“…From a simple analysis of such a system, it is obviously best to select nodes containing nonlinear receivers as the most appropriate for a possible APF connection. Therefore, in a 33-node system with three nonlinear loads [118], combinatorial optimization could be used, which would provide a very fast solution to the problem. In the publications [119,120], the authors used four loads, while in [150] there were eight of them, but still all eight were located at the ends of the branch.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

“…Some authors assume that the function f 1a leads to financial cost minimization, assuming that the cost is proportional to the APF rating [111][112][113][114][115][116][117][118][119][120]. A more sophisticated approach takes into account fixed and variable costs:…”

Section: Optimization Problem Definitions Used In Apf Placement and S...mentioning

confidence: 99%

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A Review on Optimization of Active Power Filter Placement and Sizing Methods

2022

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Distortions of current and voltage waveforms from a sinusoidal shape are, not only a source of technical problems, but also have serious economic effects. Their occurrence is related to the common use of loads with nonlinear current-voltage characteristics. These are both high-power loads (most often power electronic switching devices supplying high-power drives), but also widely used low-power loads (power supplies, chargers, energy-saving light sources). The best way to eliminate these distortions is to use active power filters. The cost of these devices is relatively high. Therefore, scientists all over the world are conducting research aimed at developing techniques for the proper placement of these devices, in order to minimize their investment costs. The best solution to this problem is to use optimization techniques. This paper compares the methods and criteria used by the authors of publications dealing with this topic. The summary also indicates a possible direction for further work.

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Section: Metaheuristic Methodsmentioning

confidence: 99%

Section: Metaheuristic Methodsmentioning

confidence: 99%

Section: Metaheuristic Methodsmentioning

confidence: 99%

Section: Optimization Problem Definitions Used In Apf Placement and S...mentioning

confidence: 99%

See 2 more Smart Citations

A Review on Optimization of Active Power Filter Placement and Sizing Methods

2022

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“…In the study by [28], it becomes evident that the results obtained demonstrate the GWO's capacity to yield highly competitive outcomes when compared to established metaheuristic techniques, such as particle swarm optimization, gravitational search, differential evolution, evolutionary programming, evolution strategy, and genetic algorithms. Recently, the efficacy of the Gray Wolf Optimizer (GWO) has been demonstrated in the realm of power system optimization [29][30][31][32][33][34][35] Given the similarities between power system optimization and thermal system optimization challenges, the utilization of GWO holds significant appeal and shows promise for enhancing the field of thermal system optimization. Consequently, the primary objective of this work lies in harnessing GWO as a tool for optimizing thermoeconomic cogeneration power plants, marking an attractive and promising avenue for research.…”

Section: Introductionmentioning

confidence: 99%

Gray Wolf Optimizer for the Cogeneration Power Plant Design Within a Framework of Commercial Software and Mathematical Platform

Araújo,

Gasperazzo,

Rocha

2023

Preprint

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In a realm where finite natural fossil fuel reservoirs coexist with escalating energy requisites and critical ecological contamination thresholds, matters pertaining to the configuration of thermal systems, encompassing energy efficacy, financial assessment, project intricacy, ecological consciousness, and fine-tuned optimization, have progressively piqued the scientific community's curiosity. Hence, thermoeconomic optimization emerges as a promising avenue for enhancing the efficiency of thermal system designs. Nevertheless, the intricacies of thermoeconomic optimization in thermal system design typically involve a multitude of components, interconnected processes, and flows, which collectively give rise to a complex system of nonlinear equations stemming from both thermodynamic and economic modeling. Moreover, the inherent objective functions in these optimization challenges are analytically daunting, characterized by traits like discontinuity, multimodality, and non-differentiability, further compounded by a multitude of decision variables. In this context, metaheuristic methods present themselves as promising and appealing tools for optimizing such intricate systems. In this study, we employ two metaheuristic methods, namely the Genetic Algorithm (GA) and the Gray Wolf Optimizer (GWO), to optimize the regenerative gas turbine cogeneration system, recognized in the literature as the CGAM problem. The thermoeconomic optimization challenge is tackled and resolved through the computational integration of a commercial software package (EES) and a mathematical platform (Matlab). Within this framework, the thermodynamic and economic modeling, as well as the thermoeconomic optimization components, are seamlessly integrated into the Engineering Equation Solver (EES). EES, in turn, calculates the thermodynamic properties for all streams within the cogeneration system while concurrently solving mass and energy balances as necessitated by the evaluation of the objective function. It is worth noting that the GA operates as an optimization tool within EES, whereas the GWO is implemented in Matlab and effectively integrated with EES. This study reveals that, despite GWO's relatively longer computational time attributable to the integration between Matlab and EES, it stands out as notably efficient in addressing the given problem, primarily owing to its reduced demand for objective function evaluations during the optimization process. Moreover, both the decision variables and the objective function tend to converge towards values closely aligned with those found in the reference literature.

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