This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25% more current THD, the switching frequency and loss per phase were reduced by 20.62% and 19.78%, respectively. The instantaneous overall power loss was also reduced by 2% due to the addition of a switching frequency term in the cost function which ensures a satisfactory empirical result for an on-grid PV inverter.

In this paper, a high sensitive photonic crystal fiber (PCF) based surface plasmon resonance (SPR) biosensor is numerically studied. In this structure, as a plasmonic material, gold (Au) is used because of its chemical activeness. And a layer of sensing medium is used outside of the fiber to make the structure effective. Any unknown biomolecular analyte can be detected by placing or flowing it on the metal surface. Guiding properties and results are investigated using Finite element method (FEM). Results show that maximum sensitivity is 4000 nm/RIU, as well as resolution, is 2.5 × 10 −5 RIU of the proposed sensor.

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A considerable amount of energy is lost by utilizing the traditional pulse width modulation (PWM) based inverters in an on-grid PV system. Therefore, a model predictive current control (MPCC) based control strategy is proposed in this research work. The controller works based on a predefined cost function. The cost function includes deviation of current from its reference and a switching frequency term to reduce the average switching frequency. All the possible control actions i.e. inverter switching states are tested against the cost function. The state which yields minimum cost is selected as an optimal control action for the inverter. Simulation results show that the proposed controller tracks the reference current accurately with a mean absolute error of 2.5% which is 30% for the PI-PWM based controller. The MPCC based inverter yields low current THD of 2.07%, whereas in traditional PI-PWM based inverter the current THD is 7.26%. The energy efficient operation of the MPCC based inverter is also verified by doing loss analysis. It is shown that conduction, switching, and harmonic losses of the inverter are reduced by 36.8%, 50%, and 91.9%, respectively, in comparison with the PIP-WM based inverter.