Unified Power quality conditioner (UPQC) consists of back to back voltage source inverters. In UPQC combination of parallel and series active power filters are used to compensate for the nonlinear load current harmonics and voltage distortions, simultaneously. For appro More
Unified Power quality conditioner (UPQC) consists of back to back voltage source inverters. In UPQC combination of parallel and series active power filters are used to compensate for the nonlinear load current harmonics and voltage distortions, simultaneously. For appropriate performance of both converters and bidirectional power flow, the DC link voltage should be at least 1.41 times larger than the line to line voltage in the high voltage part of the system; i.e. the parallel active filter. One of the determining factors for the cost of semiconductors is the maximum tolerable voltage stress. The voltage stress of the series converter increases when the DC link voltage is high. In order to overcome this deficiency, a Z-source network is added to the common structure of back to back invertors in the UPQC. It will reduce the applied DC voltage to the series active power filters significantly and decrease the cost of manufacturing. In this structure, an impedance source network is used in an AC/DC inverter to produce a buck-boost effect. Additionally, dead time has been eliminated through the use of a Z source network in the parallel active filter and thus its performance and reliability has increased impressively. In this paper, a comparison study has been conducted through necessary simulations for the performance evaluation of the common and proposed structures. The total switching device power has been used as a criteria to confirm the manufacturing cost reduction in the proposed structure.
Manuscript profile
In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infect More
In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infection mathematical formulae and ensures that the physical limits of the actuator are reached. This is accomplished by creating the concepts of model-free adaptive control, in which the relation between input and output is described using local dynamic linearized models based on quasi-partial derivatives. To determine the DDSMC law, a performance index is first defined based on the fulfillment of a discrete-time exponential reaching condition. By turning this index into a quadratic programming problem, the dynamics of the PRNN are extracted based on projection theory. The closed-loop system is explicitly determined using the optimizer output equation and the closed-loop stability analysis is evaluated using the singular value approach. The simulation results reveal that the proposed algorithm has robust performance in conducting the state variables of HIV infection to the healthy equilibrium point in the face of model uncertainty and external disturbances when compared to one of the newest control techniques.
Manuscript profile
