<p>A conventional vector control of the asynchronous machine makes an analogy of an equivalent separately excited DC machine. It offers a decoupled control of torque and flux which is perpendicular to each other hence one vector is not interfered by other parameters. So, torque and speed control is achieved in an isolated manner even though they are closely interlinked. This is implemented by aligning the rotor flux with the direct axis of the synchronously rotating reference frame. PI controllers play a key role to achieve the desired topology of the VFD. Three controllers are used in the system, flux, speed and torque controller. Tuning of flux controller is quite simple, but in case of speed and torque, it became quite tricky because the output of the speed controller is the reference signal of torque controller. Moreover, there is no distinct method to tune the controllers in the vector control system. Still, the entire high-performance dynamic response of the machine depends on the perfect tuning of those controllers. From the above analysis, it is understood that system identification is essential to tune the PI controllers. But being an asynchronous machine, to obtain system transfer function in a decoupled manner is very difficult. To overcome this problem, the proposed model will be Conventional sine PWM modulated switching pulses are used to implement variable frequency drives for induction motor. Space vector modulated PWM switching pulse is used to fire IGBT. In the case of sine, PWM modulated switching; DC bus voltage utilization is 50% whereas in space vector modulated inverter 57.73% DC Bus voltage utilization can be achieved. <strong></strong></p>

<p>Compared to a time-based maintenance schedule, condition-based maintenance provides better diagnostic information on the health condition of the different wind turbine components and subsystems. Rather than using an offline condition monitoring technique, which require the WT to be taken out of service, online condition monitoring does not require any interruption on the WT operation. The online condition monitoring system uses different types of sensors such as vibration, acoustic, temperature, current/voltage etc. Using a machine learning approach, we aim to establish a data driven fault prognosis framework. Instead of traditional wired communications, wireless communication systems such as Wireless Sensor Network have the advantages of easier installation and lower capital cost. We propose the use of WSN for collecting and transmitting the condition monitoring data to enhance the reliability of Wind Parks. Using data driven approach the collective health of the WP can be represented based on the condition of the individual wind turbines, which can be used for predicting the Remaining Useful Life of the system.</p>