Lyapunov-Function and Proportional-Resonant-Based Control Strategy for Single-Phase Grid-Connected VSI With LCL Filter PROJECT TITLE :Lyapunov-Function and Proportional-Resonant-Based Control Strategy for Single-Phase Grid-Connected VSI With LCL FilterABSTRACT:This paper presents a brand new control strategy primarily based on Lyapunov-perform and proportional-resonant (PR) controller for single-phase grid-connected LCL-filtered voltage-supply inverters (VSIs). While Lyapunov-perform-based mostly control guarantees the global stability of the system, the PR controller is utilized to method the grid current error and confirm the inverter current reference. But, it is shown that the standard Lyapunov-operate-based control (CLFBC) together with the PR control cannot damp the inherent resonance of the LCL filter. Thus, this management approach is changed by adding a capacitor voltage loop therefore as to realize the desired resonance damping. Also, a transfer operate from the reference grid current to actual grid current is formulated in terms of the LCL-filter parameters and their doable variations within the proposed management strategy. An important consequence of using the PR controller is that the need for performing 1st and second spinoff operations in the generation of inverter current reference is eliminated. Conjointly, a zero steady-state error within the grid current is guaranteed within the case of variations within the LCL-filter parameters. The laptop simulations and experimental results obtained from a three.3-kW system show that the proposed control strategy exhibits a smart performance in achieving the desired management objectives such as quick dynamic response, zero steady-state error, world stability, and sinusoidal grid current with low total harmonic distortion (THD). Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Multiport UHF RFID-Tag Antenna for Enhanced Energy Harvesting of Self-Powered Wireless Sensors An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data