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• W3034184292 endingPage "100365" @default.
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• W3034184292 abstract "Abstract In the present scenario of microgrid system, conversion of electrical energy has initiated a challenge to maintain the power quality within a satisfactory range. It can be influenced by the voltage deviation, sag/swell, unbalancing, frequency, total harmonic distortion (THD) and power factor as per nature of local loads and the condition of distributed energy resources (DERs). The relationship between power quality and the set of these variables are non-linear in nature. The existing literature show that the above mentioned parameters are not considered simultaneously for the assessment and controlling of power quality in PV based AC microgrid. To minimize the effect of these variables, a novel artificial neural network (ANN) based control approach has been proposed which can control the power quality as per IEEE/IEC standards. The proposed method has shown fast, smooth and stable operation while the performance of the same is verified with that of the proportional–integral (PI) and fuzzy-PI controllers using Matlab-Simulink software. The small size microgrid model is tested with the effect of line impedance and communication delay for the assessment of power quality parameters. This model is extended to a large size realistic microgrid structure for the feasibility of control methodology. The realistic microgrid structure is verified under the analysis of line impedance, communication delay, demand response and off-nominal conditions. The proposed control methodology is validated in a realistic microgrid structure and simulation results are presented to show the performance of proposed controller under different test conditions to identify an ANN library." @default.
• W3034184292 created "2020-06-19" @default.
• W3034184292 creator A5014410833 @default.
• W3034184292 creator A5081868321 @default.
• W3034184292 date "2020-09-01" @default.
• W3034184292 modified "2023-10-16" @default.
• W3034184292 title "Power quality control based on voltage sag/swell, unbalancing, frequency, THD and power factor using artificial neural network in PV integrated AC microgrid" @default.
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• W3034184292 doi "https://doi.org/10.1016/j.segan.2020.100365" @default.
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