Modeling, Control and Power Management Strategy of a Grid connected Hybrid Energy System

Bhuyan, Sujit Kumar; Hota, Prakash Kumar; Panda, Bhagabat

doi:10.11591/ijece.v8i3.pp1345-1356

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…MG works on grid coupled or island mode, organizing systems with private or business clients in rustic or urban territories. The regular designs of the MG system appeared in Figure and seen additionally in previous works . The DERs and the controllable loads are facilitated by the MG energy manager (MGEM).…”

Section: Introductionmentioning

confidence: 91%

“…The regular designs of the MG system appeared in Figure 1 and seen additionally in previous works. [7][8][9] The DERs and the controllable loads are facilitated by the MG energy manager (MGEM). In this MGEM, local controllers (LC) have combined with every one of the DERs and loads, which joins with the arranging of resources through interchanges foundation in a distributed manner with MGEM.…”

Section: Introductionmentioning

confidence: 99%

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An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

et al. 2019

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This paper proposes an efficient hybrid approach-based energy management strategy (EMS) for grid-connected microgrid (MG) system. The primary objective of the proposed technique is to reduce the operational electricity cost and enhanced power flow between the source side and load side subject to power flow constraints. The proposed control scheme is a consolidated execution of both the random forest (RF) and quasioppositional-chaotic symbiotic organisms search algorithm (QOCSOS), and it is named as QOCSOS-RF. Here, the QOCSOS can have the capacity to enhance the underlying irregular arrangements and joining to a superior point in the pursuit space. Likewise, the QOCSOS has prevalence in nonlinear frameworks due over the way that can insert and extrapolate the arbitrary information with high exactness. Here, the required load demand of the grid-connected MG system is continuously tracked by the RF technique.The QOCSOS optimized the perfect combination of the MG with the consideration of the predicted load demand. Furthermore, in order to reduce the influence of renewable energy forecasting errors, a two-strategy for energy management of the MG is employed. At that point, proposed model is executed in MATLAB/Simulink working platform, and the execution is assessed with the existing techniques. KEYWORDS energy management strategy, grid-connected MG, power flow constraints, random forest, quasioppositional-chaotic symbiotic organisms search algorithm How to cite this article: Chen J, Zhou Z, Karunakaran V, Zhao S. An efficient technique-based distributed energy management for hybrid MG system: A hybrid QOCSOS-RF technique. Wind Energy. 2020;23:575-592. https://doi.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

et al. 2019

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“…Each kind of voice has its exceptional qualities called highlight and the way toward removing these highlights from the individual voice is called highlight extraction. The voice highlights which are removed are contrasted and right now spared voices in the database for coordinating [13,14].…”

Section: Voice Acknowledgment Modulementioning

confidence: 99%

Design and implementation a security system for bank using voice recognition

Ali¹,

Azze²

2019

IJPEDS

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In this work, a new security system design is demonstrated based on the sound signal for bank protection. Voice Acknowledgment Module is placed on the gate of the safe. Voice Acknowledgment Module is set to perceive the capable client as indicated by his sound recurrence reaction. The plan could program to permit tow clients for greater adaptability the card location strategy is executed utilizing individual data information to permit other client in the framework. A delicate camera is accustomed to observing the developments in the structure and for the reasons for wellbeing and security. All these sensors are controlled by arduino.

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“…The Lithium-Ion Batteries (LiBs) have pulled in consideration in the previous years due to their high energy density, long life cycle, and no memory effect [1]. They are used in several applications such as renewable energy systems to capture surplus electricity, electrical and hybrid vehicles, and telephone communication [2][3][4]. However, LiBs require exceptional caution to avoid disintegration of battery performance and prevent situations that could result in severe damage or explosions.…”

Section: Introductionmentioning

confidence: 99%

Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

Boujoudar

Elmoussaoui

Lamhamdi

2019

IJECE

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In This paper, we propose an effective and online technique for modeling nd State of Charge (SoC) estimation of Lithium-Ion (Li-Ion) batteries using Feed Forward Neural Networks(FFNN) and Nonlinear Auto Regressive model with eXogenous input(NARX). The both Artificial Neural Network (ANN) are rained using the data collected from the batterycharging and discharging pro ess. The NARX network finds the needed battery model, where the input ariables are the battery terminal voltage, SoC at the previous sample, and the urrent, temperature at the present sample. The proposed method is imple mented on a Li-Ion battery cell to estimate online SoC. Simulation results show good estimation of the SoC.

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Modeling, Control and Power Management Strategy of a Grid connected Hybrid Energy System

Cited by 8 publications

References 21 publications

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

An efficient technique‐based distributed energy management for hybrid MG system: A hybrid QOCSOS‐RF technique

Design and implementation a security system for bank using voice recognition

Lithium-Ion batteries modeling and state of charge estimation using Artificial Neural Network

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