ANFIS-Based Droop Control of an AC Microgrid System: Considering Intake of Water Treatment Plant

C. Rohmingtluanga (), Subir Datta, Nidul Sinha, Taha Selim Ustun () and Akhtar Kalam ()
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C. Rohmingtluanga: Department of Electrical Engineering, Mizoram University, Aizawl 796004, Mizoram, India
Subir Datta: Department of Electrical Engineering, Mizoram University, Aizawl 796004, Mizoram, India
Nidul Sinha: Department of Electrical Engineering, NIT Silchar, Silchar 788010, Assam, India
Taha Selim Ustun: Fukushima Renewable Energy Institute, AIST (FREA), Koriyama 963-0298, Japan
Akhtar Kalam: College of Engineering and Science, Victoria University, Footscray, VIC 3011, Australia

Energies, 2022, vol. 15, issue 19, 1-24

Abstract: Provision of an efficient water supply system (WSS) is one of the top priorities of all municipals to ascertain adequate water supply to the city. Intake is the lifeline of the water supply system and largely effects the overall plant efficiency. The required power supply is generally fed from the main grid, and a diesel generator is commonly used as a power backup source. This results in high pumping cost as well as high operational cost. Moreover, due to operation of motor pumps and other auxiliary loads, frequent maintenance is required. Therefore, to avoid various challenges and to efficiently operate the intake system, microgrid concept has been introduced in this paper. Various distributed generations (DGs) such as solar photovoltaic (PV), interior permanent magnet machine (IPM) wind turbine generator and Battery energy storage system (BESS) are incorporated in the microgrid system. Additionally, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) is proposed, where P-f and Q-V droop is considered while training the ANFIS data; after successful training, the microgrid voltage and frequency are controlled as per system requirement. Simulation of the microgrid system shows good results and comparison with the generalized droop control (GDC) method is done using MATLAB/Simulink software.

Keywords: microgrid; intake; photovoltaic (PV); interior (or buried) permanent magnet machines (IPM)-based wind turbine generator; battery energy storage system (BESS); adaptive neuro-fuzzy inference system (ANFIS); droop control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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