Artificial Neural Network Based Droop-Control Technique for Accurate Power Sharing in an Islanded Microgrid

Vigneysh, T.; Kumarappan, N.

doi:10.1080/18756891.2016.1237183

Cited by 25 publications

(7 citation statements)

References 30 publications

(28 reference statements)

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“…The proposed topology is shown in Figure 1. To manage the power sharing accurately in islanded DC micro-grid, feed forward neural network (FFNN) based droop control technique was proposed [8]- [9]. In this work, a hybrid sources consisting of solar PV system, wind energy turbine, battery energy storage system and SOFC were used to feed the grid and actual modeling of such sources were used instead of having ideal sources in distributed generation.…”

Section: Introductionmentioning

confidence: 99%

GWO-based MPPT controller for grid connected Solid oxide fuel cell with high step up DC-DC converter

Ponnuru

Kumar²,

Swaroopan³

2021

IJEECS

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The solid oxide fuel cell (SOFC) is used as secondary source in micro grid application. When the renewable sources are not able meet the load requirement, Battery energy back-up system is supposed to supply the energy to meet the demand. The SOFC come into action when the state of charge (SOC) of battery energy backup becomes too low. The SOFC parameters are assumed as constant during its operation but those operating parameters are not practically constant. The operating parameters vary widely which will influence the output voltage of SOFC. Hence an optimum power extracting controller is being implemented to ensure maximum power under dynamic operating condition. A high step up converter is designed to boost the output voltage of SOFC whose steady state analysis is studied in this work. The switches of high step up converter are triggered using optimum power controller using grey wolf based optimization algorithm. The proposed controller performance is compared with conventional particle swarm optimization based controller. The simulation is carried out using MATLAB/simulink and results discussed.

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Section: Introductionmentioning

confidence: 99%

GWO-based MPPT controller for grid connected Solid oxide fuel cell with high step up DC-DC converter

Ponnuru

Kumar²,

Swaroopan³

2021

IJEECS

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“…The bidirectional converter is controlled to regulate the DC_Bus voltage within safe limits and simultaneously impose a given energy flow between the battery and the DC_Bus 1 . Many papers focused on regulating of the DC_Bus voltage by the control of bidirectional converter 5‐14 . The PID controllers are the most common used methods for the control of engineering application, which are processes the error signal into input signal by the aid of proportional factor (P), integrative action (I), and differential action (D).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, various artificial models implemented as controllers in several converters 8‐14 . One of the first examples of the use of ANN approach presented by Reference 8 based on feed forward neural network to overcome the power‐sharing issues and to regulate islanded micro grid voltage. Another study develop an ANN‐based control for DC/DC converters and integrate it with droop mechanism for control of a standalone DC micro grid 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

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Intelligent controller based energy management for stand‐alone power system using artificial neural network

Boujoudar

Azeroual

Moussaoui

et al. 2020

Int Trans Electr Energ Syst

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Summary Standalone Power Systems (SPS), formerly known as Micro Power Systems, is an off‐grid electricity system for locations that are not fitted with an electricity generation. Typical SPS include one or more renewable electricity sources, energy storage, and regulation. In this paper, supervision of hybrid Photovoltaic system and battery storage is presented. The power balance of the hybrid system is made on an intelligent supervisor based on Artificial Neural Network Controller (ANNC). The main purpose of this paper is the control of DC/DC Bidirectional Converter (DBC), which connect the Li‐ion battery with the DC_Bus of Stand‐alone Power Systems (SPS). Based on the use of artificial neural network controller, which are effective tools for highly nonlinear system. The proposed technique does not require a pre‐defined model of power DBC only depend on power and voltage system data. Simulation for stand‐alone systems performed at Matlab Simulink. The comparison with PID controller for the result obtained of DC_Bus voltage, and power demanded, demonstrate the robustness, and satisfactory performance of the proposed control strategy.

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“…A novel hybrid carrier-based PWM technique was developed for parallel-interleaved two-level three-phase VSIs, treated as a unified three-level inverter to mitigate line current ripple [8]. In an islanded microgrid, an FFNN (Feedforward Neural Network) is utilized to achieve equal power sharing among PCI, controlling voltage and frequency Website: www.ijeer.forexjournal.co.in Improved Power Sharing Strategy for Parallel nnected [9]. A modified droop control technique in a solar-powered microgrid was developed for balanced real and reactive power sharing among inverters [10].…”

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confidence: 99%

Improved Power Sharing Strategy for Parallel Connected Inverters in Standalone Micro-grid

Gaddameedhi,

Susheela

2024

IJEER

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The primary goal of integrating alternative energy systems such as solar and wind turbines into the power grid using power electronic devices is to meet the growing energy demands. Connecting inverters in parallel effectively enhance power capacity, reliability, and overall system efficiency. However, an uneven power distribution among the inverters is a significant limitation in these parallel connected inverters (PCI). This study focuses on a distributed generation (DG) unit comprising a solar photovoltaic system (SPV) and a battery energy storage system (BESS) connected to voltage source inverters (VSI) 1 and 2. The proposed approach aims to achieve uniform load/power distribution among the inverters with power management, maintaining a constant DC link voltage despite variations in solar irradiation and temperature. Additionally, the strategy targets the reduction of total harmonic distortion (THD) in the load current. The conventional droop control method is restricted in its ability to achieve accurate and uniform power distribution during load changes. An enhanced P-f and Q-E-based droop control technique (EDCT) and a fuzzy logic controller (FLC) have been proposed to address these challenges. Performance analysis using MATLAB/Simulink was conducted with two test cases, and a comparative assessment was carried out with a conventional controller, such as a Proportional Integral Controller (PIC) and Sliding Mode Controller (SMC), to showcase the effectiveness of the developed control technique. The proposed control method ensures equal sharing of active power of 2.5 and 4kW, 2.5 and 7.5kW and reactive powers of 2.5 and 4Vars, 2.5 and 7.5Vars among the inverters for the two cases even during the load change at t=0.5seconds. The settling time for DC link voltage and THD is effectively diminished to 0.03 and 0.25 seconds and 1.18% and 2.87% for the two test case studies of the proposed method, which are much lower than those of existing methods available in the literature.

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Artificial Neural Network Based Droop-Control Technique for Accurate Power Sharing in an Islanded Microgrid

Cited by 25 publications

References 30 publications

GWO-based MPPT controller for grid connected Solid oxide fuel cell with high step up DC-DC converter

GWO-based MPPT controller for grid connected Solid oxide fuel cell with high step up DC-DC converter

Intelligent controller based energy management for stand‐alone power system using artificial neural network

Improved Power Sharing Strategy for Parallel Connected Inverters in Standalone Micro-grid

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