Power flow management in micro grid through renewable energy sources using a hybrid modified dragonfly algorithm with bat search algorithm

Kumaravel, Sureshkumar; Vijayakumar, P.

doi:10.1016/j.energy.2019.06.029

Cited by 61 publications

(29 citation statements)

References 35 publications

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“…Figure 28 shows that the WDDA is not as accurate as the DA, but it is worse than other comparison algorithms. Figures 19,21,23,25,27,29,31, and 33 are the variance graphs of f 9 ∼f 16 . Through the comparison of these experimental results, it can be seen that the WDDA has the characteristics of fast convergence and strong optimization ability in optimizing most high-dimensional multimodal function optimization.…”

Section: High-dimensional Unimodal Function Test Resultsmentioning

confidence: 99%

“…28 Sureshkumar et al proposed a hybrid method based on improved DA and bat search algorithms for the management of power flow of mixed renewable energy in microgrids. 29 Gonal et al proposed a hybrid method based on the combination of DA and BA to solve the control problem of wind and solar system grid connection. 30 Shilaja et al proposed a hybrid method based on DA and enhanced gray wolf optimization algorithm to solve the optimal power flow problem.…”

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

“…Kumar et al proposed a hybrid based on the CSA and the integrated fraction dragonfly algorithm for load balancing in cloud computing environments 28 . Sureshkumar et al proposed a hybrid method based on improved DA and bat search algorithms for the management of power flow of mixed renewable energy in microgrids 29 . Gonal et al proposed a hybrid method based on the combination of DA and BA to solve the control problem of wind and solar system grid connection 30 .…”

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Wind driven dragonfly algorithm for global optimization

Zhong

Zhou

Luo

et al. 2020

Concurrency and Computation

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Dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.

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Section: High-dimensional Unimodal Function Test Resultsmentioning

confidence: 99%

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

Section: Related Work With Da Hybrid Algorithmmentioning

confidence: 99%

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Wind driven dragonfly algorithm for global optimization

Zhong

Zhou

Luo

et al. 2020

Concurrency and Computation

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“…Control signals of voltage source inverter (VSI) based on the power variations was predicted by RFA. Managing the power flow with the association of HRES system using hybrid approach called modified dragonfly algorithm (MDA) with bat search algorithm (BSA) named as MDABSA technique was established by Sureshkumar and Ponnusamy 27 . MDA develops the VSI control signals based on the variation of power between source and load side.…”

Section: Introductionmentioning

confidence: 99%

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

Kumari

2020

Int J Numerical Modelling

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This dissertation shows energy scheduling in grid-connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN-BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid-connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.

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“…A MAS is proposed by Keshta et al to achieve optimal energy management for voltage regulation and to enhance the stability of a system under different weather conditions and load perturbations for two connected micro‐grids. An efficient hybrid approach is presented by Sureshkumar et al for the power flow management of the hybrid renewable energy system‐connected smart grid system. In the proposed approach, the control signals of the voltage source are developed by the MDA based on the variety of power exchange between the source side and load side.…”

Section: Introductionmentioning

confidence: 99%

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Mir

Dayyani

Sutikno

et al. 2019

Computational Intelligence

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There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.

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Power flow management in micro grid through renewable energy sources using a hybrid modified dragonfly algorithm with bat search algorithm

Cited by 61 publications

References 35 publications

Wind driven dragonfly algorithm for global optimization

Wind driven dragonfly algorithm for global optimization

A hybrid RBFNN–BBMO methodology for robust energy management in grid‐Connected microgrid

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

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