Dynamic Model of Microturbine Generation System for Grid Connected/Islanding Operation

Gaonkar, D. N.; Patel, R. N.; Pillai, G. N.

doi:10.1109/icit.2006.372229

Cited by 42 publications

(19 citation statements)

References 13 publications

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“…The model has been adapted from [6] and is based on the model developed by Rowen in [7] for heavy duty gas turbines. Figure 3 shows the microturbine model.…”

Section: A Prime Movermentioning

confidence: 99%

Modelling and Simulation of a Microturbine during Transient Events

Eguia¹,

Zamora²,

Torres³

et al. 2024

RE&PQJ

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“…The model has been adapted from [6] and is based on the model developed by Rowen in [7] for heavy duty gas turbines. Figure 3 shows the microturbine model.…”

Section: A Prime Movermentioning

confidence: 99%

Modelling and Simulation of a Microturbine during Transient Events

Eguia¹,

Zamora²,

Torres³

et al. 2024

RE&PQJ

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“…Figure A4 illustrates the model of the gas turbine. The model includes governor control, fuel control and the dynamics of the gas-turbine engine [14]. Figure A4.…”

Section: A4 Gas Turbine Generatormentioning

confidence: 99%

Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System

et al. 2014

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Microgrids are a highly efficient means of embedding distributed generation sources in a power system. However, if a fault occurs inside or outside the microgrid, the microgrid should be immediately disconnected from the main grid using a static switch installed at the secondary side of the main transformer near the point of common coupling (PCC). The static switch should have a reliable module implemented in a chip to detect/locate the fault and activate the breaker to open the circuit immediately. This paper proposes a novel approach to design this module in a static switch using the discrete wavelet transform (DWT) and adaptive network-based fuzzy inference system (ANFIS). The wavelet coefficient of the fault voltage and the inference results of ANFIS with the wavelet energy of the fault current at the secondary side of the main transformer determine the control action (open or close) of a static switch. The ANFIS identifies the faulty zones inside or outside the microgrid. The proposed method is applied to the first outdoor microgrid test bed in Taiwan, with a generation capacity of 360.5 kW. This microgrid test bed is studied using the real-time simulator eMegaSim developed by Opal-RT Technology Inc. (Montreal, QC, Canada). The proposed method based on DWT and ANFIS is implemented in a field programmable gate array (FPGA) by using the Xilinx System Generator. Simulation results reveal that the proposed method is efficient and applicable in the real-time control environment of a power system. OPEN ACCESSEnergies 2014, 7 2659 Keywords: microgrid; static switch; wavelet transform; adaptive network-based fuzzy inference system (ANFIS)

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“…This model includes the speed governor, acceleration control block, temperature control and fuel system control. MTG details are presented in [29]. The power producer is a synchronous generator with a permanent magnet, which has two poles and a salient pole rotor.…”

Section: Mtg System Configurationmentioning

confidence: 99%

“…In this paper, the simulation and structure of MTG and flywheel have been presented, as investigated in [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

Izadbakhsh¹,

Rezvani²,

Gandomkar³

2014

Archives of Electrical Engineering

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Abstract:The microgrid (MG) technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG), photovoltaic (PV), microturbine generation (MTG) systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique was used by an adaptive neuro-fuzzy inference system (ANFIS); also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.

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Dynamic Model of Microturbine Generation System for Grid Connected/Islanding Operation

Cited by 42 publications

References 13 publications

Modelling and Simulation of a Microturbine during Transient Events

Modelling and Simulation of a Microturbine during Transient Events

Fault Detection and Location by Static Switches in Microgrids Using Wavelet Transform and Adaptive Network-Based Fuzzy Inference System

Improvement of Microgrid Dynamic Performance under Fault Circumstances using ANFIS for Fast Varying Solar Radiation and Fuzzy Logic Controller for Wind System

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