PSO based controller algorithm for optimal allocation &amp;amp; setting of fuel cell in a wind &amp;#x2014; PV integrated power system for maximizing loadability

Joseph, Seena; Skariah, Emil Ninan; Joseph, Tibin; Sreedharan, Sasidharan; Chittesh, V C; Das, Priyanka; Vishnu, J

doi:10.1109/icage.2014.7050135

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Stochastic algorithms based methods were applied for selecting the suitable sizing and setting of shunt FACTS controllers to enhance the voltage stability [3][4][5][6][7][8][9] . Application of Genetic Algorithm (GA) for solving a range of power system problems has been reported in the literature [8][9][10][11] .…”

Section: Indices and Sets I Jmentioning

confidence: 99%

Power system loading margin enhancement by optimal STATCOM integration – A case study

Sreedharan¹,

Joseph

Joseph³

et al. 2020

Computers & Electrical Engineering

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Safe and secure network operation with acceptable voltage level has become a challenging task for utilities requiring corrective measures to be implemented. Network upgrades using Flexible Alternating Current Transmission System devices are being considered to serve this purpose. To this end, static loading margin enhancement by optimal static synchronous compensator (STATCOM) allocation to enhance the power transfer capability with minimal voltage variation is presented. Maximum loadability is formulated as an optimization problem, subjected to voltage and small-signal stability constraints. Stability indices are presented and incorporated with the optimization problem to ensure secure operation under maximum loading. The scheme is executed with the IEEE system and an Indian utility network. Improved voltage regulation with different loading condition was achieved for both test networks, with the service rendered by the optimally placed STATCOM. Moreover, it facilitates an additional 50% capacity release in both test systems for hosting the active power and loads.

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Section: Indices and Sets I Jmentioning

confidence: 99%

Power system loading margin enhancement by optimal STATCOM integration – A case study

Sreedharan¹,

Joseph

Joseph³

et al. 2020

Computers & Electrical Engineering

Self Cite

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“…The authors have presented a multi-objective function that incorporates network losses, bus voltage and energy cost in their work [39,40]. The utilization of a particle swarm optimization (PSO) algorithm has been documented in previous studies [41,42]. Previous research have shown the application of a genetic algorithm (GA) [43,44].…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost

Hany,

Mahmoud,

Osman

et al. 2024

PLoS ONE

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The enhancement of energy efficiency in a distribution network can be attained through the adding of energy storage systems (ESSs). The strategic placement and appropriate sizing of these systems have the potential to significantly enhance the overall performance of the network. An appropriately dimensioned and strategically located energy storage system has the potential to effectively address peak energy demand, optimize the addition of renewable and distributed energy sources, assist in managing the power quality and reduce the expenses associated with expanding distribution networks. This study proposes an efficient approach utilizing the Dandelion Optimizer (DO) to find the optimal placement and sizing of ESSs in a distribution network. The goal is to reduce the overall annual cost of the system, which includes expenses related to power losses, voltage deviation, and peak load damand. The methods outlined in this study is implemented on the IEEE 33 bus distribution system. The outcomes obtained from the proposed DO are contrasted with those of the original system so as to illustrate the impact of ESSs location on both the overall cost and voltage profile. Furthermore, a comparison is made between the outcomes of the Ant Lion Optimizer (ALO) and the intended Design of Experiment DO, revealing that the DO has obtained greater savings in comparison to the ALO. The recommended methodology’s simplicity and efficacy in resolving the researched optimization issue make the acquired locations and sizes of ESSs favorable for implementation within the system.

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“…The results showed that the proposed method can reduce grid-connected wind power fluctuations and limit system faults in power generation. Joseph et al [25] proposed the use of a PSO method to maximize the system loadability within stability margins. Results showed that PSO improved the load carrying capacity when compared to the controller system installed in the plant.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

et al. 2021

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In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.

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PSO based controller algorithm for optimal allocation & setting of fuel cell in a wind — PV integrated power system for maximizing loadability

Cited by 5 publications

References 18 publications

Power system loading margin enhancement by optimal STATCOM integration – A case study

Power system loading margin enhancement by optimal STATCOM integration – A case study

Optimal allocation of distributed energy storage systems to enhance voltage stability and minimize total cost

Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms

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