Abstract: In this paper, supply air pressure of HVAC system has been modeled with second-order transfer function plus dead-time. In HVAC system, the desired input has step changes, and the output of proposed control system should be able to follow the input reference, so the idea of using model based predictive control is proceeded and designed in this paper. The closed loop control system is implemented in MATLAB software and the simulation results are provided. The simulation results show that the model based predictive control is able to control the plant properly.
Abstract: Optimization and control of reactive power
distribution in the power systems leads to the better operation of the
reactive power resources. Reactive power control reduces
considerably the power losses and effective loads and improves the
power factor of the power systems. Another important reason of the
reactive power control is improving the voltage profile of the power
system. In this paper, voltage and reactive power control using
Neural Network techniques have been applied to the 33 shines-
Tehran Electric Company. In this suggested ANN, the voltages of PQ
shines have been considered as the input of the ANN. Also, the
generators voltages, tap transformers and shunt compensators have
been considered as the output of ANN. Results of this techniques
have been compared with the Linear Programming. Minimization of
the transmission line power losses has been considered as the
objective function of the linear programming technique. The
comparison of the results of the ANN technique with the LP shows
that the ANN technique improves the precision and reduces the
computation time. ANN technique also has a simple structure and
this causes to use the operator experience.