In this paper, the output frequency of a self excited induction generator (SEIG) driven by wind turbine and supplies static load are controlled. The principle connections of wind energy conversion are presented. The dynamic modeling of the wind turbine and its linearization are derived. The PID controller which employed for turbine rotor speed control and hence the frequency regulation is proposed. The block diagram of the proposed speed control system which consists of speed controller, actuator model and the turbine linearized model is simulated by Matlab-Simulink software package.
This paper presents the design and simulation of air-fuel percentage sensors in drone engine control using Matlab. The applications of sensor engineering system have been pioneer in technology development and advancement of automated machine as complex systems. The integration of drone fuel sensor system is the major series components such as injector, pumps and switches. The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring. The sensor system is improved and virtualized in Simulink block set by varying the parameters with high range to observe the fuel utilization curves and extract the validated results. The obtained results show that the possibility of engine operation in critical conditions such as takeoff, landing, sharp maneuver and performance is applicable to turn off the system in case of break down in the sensor to ensure the safety of drone engine.
HIGHLIGHTS
The drone engine fuel rate sensor is designed and examined to determine the air-to-fuel ratio
The suggested model is tuned to interface drone fuel system with fuel flow in order to optimize efficient monitoring
The obtained results show that the possibility of using engine with different failure mode and fault considerations
The represented control structure is simple, efficient and provides the required air-to-fuel ratio
Power stations must supply the electrical load demands to achieve optimal power system operation. To meet the future load, the power system dispatcher use load forecasting techniques to schedule unit generation resources. In this paper the short term load forecasting (STLF) using feed forward Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) techniques for Iraqi power system (IPS) is presented. The ANN and MLR techniques are used to forecast one day ahead load for summer and winter season. The ANN gives a very small mean absolute percentage error (MAPE) compared with MLR but it takes a longer time for training process.
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