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The performance of a wind energy conversion system based on a vertical axis wind turbine and a permanent magnet synchronous generator was evaluated in a computational study. A power point tracking approach was used to determine the maximum output power from the system. The technique used in the study utilised the voltage and current from the generator as input to generate a reference voltage, which was then compared with the direct current output voltage obtained at the load resistor. A proportional integral controller was used to tune the error between the reference and output voltage. The generated output was then compared with a pulse generator before being fed to the insulated-gate bipolar transistor switch as a pulse width modulation signal. In the software, a system model was designed, and its simulation analysis was done. The input parameters for wind turbines, generator and boost converters were selected to achieve optimal output in terms of voltage, current, power, rotor speed, power coefficient, electromagnetic and mechanical torque. The computational results of the simulated wind energy conversion system were gathered and its performance examined.

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.